Data Analysis for Russian MoTR Reading Data and ET data
shhh <- suppressPackageStartupMessages # It's a library, so shhh!
shhh(library( mgcv ))
shhh(library(dplyr))
shhh(library(ggplot2))
shhh(library(lme4))
shhh(library(tidymv))
shhh(library(gamlss))
shhh(library(gsubfn))
shhh(library(lmerTest))
shhh(library(tidyverse))
shhh(library(boot))
shhh(library(rsample))
shhh(library(plotrix))
shhh(library(ggrepel))
shhh(library(mgcv))
shhh(library(brms))
shhh(library(bayesplot))
shhh(library(tidyr))
shhh(library(car))
shhh(library(HDInterval))
shhh(library(gridExtra))
shhh(library(posterior))
shhh(library(readxl))
shhh(library(stringr))
shhh(library(loo))
shhh(library(MASS))
shhh(library(hypr))
shhh(library(designr))
shhh(library(afex))
shhh(library(coda))
shhh(library(rstan))
shhh(library(rstantools))
rstan_options(auto_write=TRUE)
options(mc.cores=parallel::detectCores())
rstan_options(auto_write = TRUE)
theme_set(theme_bw())
options(digits=4)
options(scipen=999)
set.seed(444)
Read in ET Data
file_list <- list.files("/Users/cui/Documents/uzh/PhD/Projects/Russian_Agreement/russian_gender/ref/Eyetracking/", pattern = "*.xlsx", full.names = TRUE)
et_raw <- file_list %>%
lapply(read_excel) %>%
bind_rows()clean ET raw data
select_meas <- c("SFD", "total_duration", "gaze_duration", "FPFix", "go_past_time", "FPReg", "RegIn")
et <- et_raw %>%
dplyr::select(IA_LABEL, item, word.id, list, RECORDING_SESSION_LABEL, SFD, IA_DWELL_TIME, IA_FIRST_RUN_DWELL_TIME, IA_FIRST_FIX_PROGRESSIVE, IA_SELECTIVE_REGRESSION_PATH_DURATION, IA_REGRESSION_OUT, IA_REGRESSION_IN, gender_match, part, target_gender, type, Region, condition, ACCURACY, animacy) %>%
rename(
word = IA_LABEL,
item_id = item,
word_nr = word.id,
subj_id = RECORDING_SESSION_LABEL,
total_duration = IA_DWELL_TIME,
gaze_duration = IA_FIRST_RUN_DWELL_TIME,
first_pass_fix = IA_FIRST_FIX_PROGRESSIVE,
go_past_time = IA_SELECTIVE_REGRESSION_PATH_DURATION,
FPReg = IA_REGRESSION_OUT,
RegIn = IA_REGRESSION_IN,
AOI_id = Region
) %>%
filter(subj_id != "russ34") %>% # russ34 has acc 0.6 according to the calculation below.
mutate(
go_past_time = as.numeric(go_past_time),
SFD = if_else(first_pass_fix == 1, SFD, 0),
gaze_duration = if_else(first_pass_fix == 1, gaze_duration, 0),
go_past_time = if_else(first_pass_fix == 1, go_past_time, 0),
) %>%
rename(FPFix = first_pass_fix) %>%
mutate(
FPReg = ifelse(gaze_duration==0, NA, FPReg),
FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
gather(measure, value, select_meas) %>%
mutate(
value = as.numeric(value),
tgt_zero = if_else(measure %in% c("SFD", "gaze_duration", "go_past_time", "total_duration") & value == 0, F, T)) %>%
filter(tgt_zero != F) %>%
dplyr::select(-tgt_zero, -condition) %>%
mutate(item_id = as.factor(item_id),
subj_id = as.factor(subj_id)) %>%
spread(measure, value) %>%
# Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
# We filter outliers only for aesthetic reasons in plotting.
gather(measure, value, c("SFD", "gaze_duration", "go_past_time", "total_duration")) %>%
mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
filter(outlier == FALSE) %>%
dplyr::select(-outlier) %>%
spread(measure, value) %>%
gather(measure, value, select_meas) %>%
mutate(cond = case_when(
target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
)) %>%
# filter(animacy %in% c("Inanim", "inanim")) %>%
dplyr::select(-list, -part, -animacy)
et Read in MoTR Data
# The path to the data
data_path <- "./data/"
data_names <- list.files(data_path)
# Read in the data from each participant and add to the data frame
motr_df <- data.frame()
for(name in data_names){
subj <- gsub("reader_", "", gsub("_reading_measures.csv", "", name))
temp_df <- read.csv(paste0(data_path, "/", name)) %>% mutate(subj_id = subj)
motr_df <- rbind(motr_df, temp_df)
}
motr_df <- motr_df %>% mutate(word_len = nchar(word),
word_length = scale(word_len)[,1]) %>%
group_by(subj_id, item_id) %>%
arrange(subj_id, item_id) %>%
mutate(word_len_pre1 = lag(word_length, n = 1),
word_len_pre2 = lag(word_length, n = 2)) %>%
ungroup()
# Clean the data
motr <- motr_df %>%
# filter(subj_id != 171) %>% # acc = 0.8
filter(! list %in% c(98, 99)) %>% # filter practice and filler items
mutate(skip = ifelse(total_duration==0, 1, 0),
FPReg = ifelse(gaze_duration==0, NA, FPReg),
FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
filter(skip == 0) %>%
gather(measure, value, 18:26) %>%
mutate(tgt_zero = if_else(measure %in% c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration") & value == 0, F, T)) %>%
filter(tgt_zero != F) %>%
dplyr::select(-tgt_zero, -cond_id, -skip, -word_len) %>%
mutate(item_id = as.factor(item_id),
subj_id = as.factor(subj_id)) %>%
spread(measure, value) %>%
# Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
# We filter outliers only for aesthetic reasons in plotting.
gather(measure, value, c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration")) %>%
mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
filter(outlier == FALSE) %>%
dplyr::select(-outlier) %>%
spread(measure, value) %>%
gather(measure, value, 21:29) %>%
mutate(cond = case_when(
target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
)) %>%
dplyr::select(-list, -part, -type_id, -orig_item_number, -case, -animacy, -response_true, -response_chosen) %>%
mutate(word = str_replace_all(word, "\\.", "")) %>%
rowwise() %>%
mutate(log_freq = ifelse(word %in% et_raw$IA_LABEL,
et_raw$lg_frequency[match(word, et_raw$IA_LABEL)],
NA_real_)) %>%
ungroup()
# View(motr)
ACC by participant
motr_acc <- motr %>% dplyr::select(item_id, cond, subj_id, correctness) %>%
filter(correctness != 99) %>%
distinct()
motr_acc_summary <- motr_acc %>%
group_by(subj_id) %>%
summarise(mean_correctness = mean(correctness),
sd_correctness = sd(correctness),
count = n())
# only subj_id 171 get acc = 0.8; others all > 0.88 (incl. fillers)
# write.csv(motr_acc_summary, "./stats/correctness_summary.csv", row.names = FALSE)
et_acc <- et %>% dplyr::select(all_of(c("item_id", "subj_id", "cond", "ACCURACY"))) %>%
# filter(ACCURACY != -1) %>%
distinct() %>%
mutate(correctness = as.numeric(unlist(ACCURACY)))
et_acc_summary <- et_acc %>%
group_by(subj_id) %>%
summarise(mean_correctness = mean(correctness),
sd_correctness = sd(correctness),
count = n())
# only subj_id russ34 get acc = 0.6; others all > 0.8 (excl. fillers)ACC by item
motr_acc_cond <- motr_acc %>%
group_by(cond) %>%
summarise(
mean_correctness = round(mean(correctness), 2),
sd_correctness = round(sd(correctness), 2),
count = n()
)
motr_acc_cond
et_acc_cond <- et_acc %>%
group_by(cond) %>%
summarise(
mean_correctness = round(mean(correctness), 2),
sd_correctness = round(sd(correctness), 2),
count = n()
)
et_acc_condRESEARCH QUESTIONS:
Are RTs significantly different between gender-match and gender-mismatch conditions? ==> main effect of grammaticality (gender match or not)
Are RTs different in Masculine versus Feminine sentence conditions? ==> main effect of gender of target word
Are RTs affected by lexical category (whether different lexical categories of the agreeing element will make the processing more difficult or not)? –> ADJ(adj & pre_adj) v.s. VERB ==> main effect of lexical type of sentences.
Are RTs affected by feature matching mechanism(whether agreeing element instantiates internal v.s. external agreement will make a difference in processing difficulty)? –> External (verb & predicative adjective) v.s. Internal (modifying adjective) ==> main effect of syntax type of sentences.
Whether the effect of grammaticality is modulated by lexical category? –> Is the RT difference caused by grammaticality effect affected by the target word being Adj or Verb? ==> interaction between grammaticality and lexical type
Whether the effect of grammaticality depends on the feature matching mechanism of the sentence? –> Is the RT difference caused by grammaticality effect affected by the mechanism being external or internal? ==> interaction between grammaticality and feature matching mechanism
Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between lexical category (Adj v.s. Verb)? ==> 3-way interaction between grammaticality, gender and lexical category
Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between feature matching mechanism (External v.s. Internal)? ==> 3-way interaction between grammaticality, gender and feature matching mechanism
contrast coding
# check conditions
et$cond <- factor(et$cond)
levels(et$cond) [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"motr$cond <- factor(motr$cond)
levels(motr$cond) [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"Create hypothesis matrix from RQ
## sol1
X_H <- matrix(c(1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12, # Intercept
1/6,1/6,1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6, # Main effect of grammaticality
1/6,1/6,1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6, # Main effect of gender
1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8, # Main effect of feature matching
-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8, # Main effect of lexical category
1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6, # gram x gen
1/4,-1/8,-1/8,-1/4,1/8,1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8, # gram x synt
-1/8,1/4,-1/8,1/8,-1/4,1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8, # gram x lex
1/4,-1/8,-1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8,-1/4,1/8,1/8, #gen x synt
-1/8,1/4,-1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8,1/8,-1/4,1/8, # gen x lex
1/2,-1/4,-1/4,-1/2,1/4,1/4,-1/2,1/4,1/4,1/2,-1/4,-1/4, # gram x gen x synt
-1/4,1/2,-1/4,1/4,-1/2,1/4,1/4,-1/2,1/4,-1/4,1/2,-1/4 # gram x gen x lex
), byrow=TRUE, nrow = 12)
# X_H
# rowSums(X_H) # ensure centering
X_C = ginv(X_H)
rownames(X_C) <- c('a','b','c','d','e','f','g','h', 'i', 'j', 'k', 'l')
colnames(X_C) <- c('Int','Gram','Gen','Lex','Synt','Gram_x_Gen','Gram_x_Lex','Gram_x_Synt','Gen_x_Lex','Gen_x_synt','Gram_x_Gen_Lex','Gram_x_Gen_Synt')
X_C_bar <- X_C[,2:ncol(X_C)]
fractions(X_C_bar) Gram Gen Lex Synt Gram_x_Gen Gram_x_Lex Gram_x_Synt Gen_x_Lex Gen_x_synt Gram_x_Gen_Lex
a 1/2 1/2 2/3 0 1/2 2/3 0 2/3 0 1/3
b 1/2 1/2 0 2/3 1/2 0 2/3 0 2/3 0
c 1/2 1/2 -2/3 -2/3 1/2 -2/3 -2/3 -2/3 -2/3 -1/3
d -1/2 1/2 2/3 0 -1/2 -2/3 0 2/3 0 -1/3
e -1/2 1/2 0 2/3 -1/2 0 -2/3 0 2/3 0
f -1/2 1/2 -2/3 -2/3 -1/2 2/3 2/3 -2/3 -2/3 1/3
g 1/2 -1/2 2/3 0 -1/2 2/3 0 -2/3 0 -1/3
h 1/2 -1/2 0 2/3 -1/2 0 2/3 0 -2/3 0
i 1/2 -1/2 -2/3 -2/3 -1/2 -2/3 -2/3 2/3 2/3 1/3
j -1/2 -1/2 2/3 0 1/2 -2/3 0 -2/3 0 1/3
k -1/2 -1/2 0 2/3 1/2 0 -2/3 0 -2/3 0
l -1/2 -1/2 -2/3 -2/3 1/2 2/3 2/3 2/3 2/3 -1/3
Gram_x_Gen_Synt
a 0
b 1/3
c -1/3
d 0
e -1/3
f 1/3
g 0
h -1/3
i 1/3
j 0
k 1/3
l -1/3 contr_motr <- motr %>%
mutate(
#--------------------- main effects ---------------------
Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality
Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching (a vs pv)
Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (ap vs v)
#--------------------- 2-way interactions ---------------------
Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
#--------------------- 3 way interection ---------------------
Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
) %>% spread(measure, value) #%>%
# # filter(word_nr == 3)
# filter(AOI_id == "R3")
contr_motr
contr_et <- et %>%
mutate(
#--------------------- main effects ---------------------
Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality
Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching (a vs pv)
Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (v vs ap)
#--------------------- 2-way interactions ---------------------
Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
#--------------------- 3 way interection ---------------------
Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
) %>% spread(measure, value) %>%
rename(RegIn_incl = RegIn)
# View(contr_et)write.csv(contr_et, "./stats/et_reading_measures_contrast_coded.csv", row.names = FALSE)
write.csv(contr_motr, "./stats/motr_reading_measures_contrast_coded.csv", row.names = FALSE)## sol2 --> try hypr package, also for sanity check
hypothesis_matrix <- hypr(
Gram = (a+b+c+g+h+i)/6 ~ (d+e+f+j+k+l)/6,
Gen = (a+b+c+d+e+f)/6 ~ (g+h+i+j+k+l)/6,
Synt = (a+d+g+j)/4 ~ (b+c+e+f+h+i+k+l)/8,
Lex = (b+e+h+k)/4 ~ (a+c+d+f+g+i+j+l)/8,
# Gram_x_Gen = ((a+b+c)/3-(d+e+f)/3)/2 ~ ((g+h+i)/3-(j+k+l)/3)/2,
Gram_x_Synt = ((e+f+k+l)/4-(d+j)/2)/2 ~ ((b+c+h+i)/4-(a+g)/2)/2 ,
Gram_x_Lex = ((d+f+j+l)/4-(e+k)/2)/2 ~ ((a+c+g+i)/4-(b+h)/2)/2,
# Gen_x_Synt = ((h+i+k+l)/4-(g+j)/2)/2 ~ ((b+c+e+f)/4-(a+d)/2)/2,
# Gen_x_Lex = ((g+i+j+l)/4-(h+k)/2)/2 ~ ((a+c+d+f)/4-(b+e)/2)/2,
Gram_x_Gen_x_Synt = (((h+i)/2-g)-((k+l)/2-j))/2 ~ (((b+c)/2-a)-((e+f)/2-d))/2,
Gram_x_Gen_x_Lex = (((g+i)/2-h)-((j+l)/2-k))/2 ~ (((a+c)/2-b)-((d+f)/2-e))/2
)
# Display the matrix
hypothesis_matrixhypr object containing 8 null hypotheses:
H0.Gram: 0 = (a + b + c + g + h + i - d - e - f - j - k - l)/6
H0.Gen: 0 = (a + b + c + d + e + f - g - h - i - j - k - l)/6
H0.Synt: 0 = (a + d + g + j - 1/2*b - 1/2*c - 1/2*e - 1/2*f - 1/2*h - 1/2*i - 1/2*k - 1/2*l)/4
H0.Lex: 0 = (b + e + h + k - 1/2*a - 1/2*c - 1/2*d - 1/2*f - 1/2*g - 1/2*i - 1/2*j - 1/2*l)/4
H0.Gram_x_Synt: 0 = (1/2*e + 1/2*f + 1/2*k + 1/2*l - d - j - 1/2*b - 1/2*c - 1/2*h - 1/2*i + a + g)/4
H0.Gram_x_Lex: 0 = (1/2*d + 1/2*f + 1/2*j + 1/2*l - e - k - 1/2*a - 1/2*c - 1/2*g - 1/2*i + b + h)/4
H0.Gram_x_Gen_x_Synt: 0 = (1/2*h + 1/2*i - g - 1/2*k - 1/2*l + j - 1/2*b - 1/2*c + a + 1/2*e + 1/2*f - d)/2
H0.Gram_x_Gen_x_Lex: 0 = (1/2*g + 1/2*i - h - 1/2*j - 1/2*l + k - 1/2*a - 1/2*c + b + 1/2*d + 1/2*f - e)/2
Call:
hypr(Gram = ~1/6 * a + 1/6 * b + 1/6 * c + 1/6 * g + 1/6 * h +
1/6 * i - 1/6 * d - 1/6 * e - 1/6 * f - 1/6 * j - 1/6 * k -
1/6 * l, Gen = ~1/6 * a + 1/6 * b + 1/6 * c + 1/6 * d + 1/6 *
e + 1/6 * f - 1/6 * g - 1/6 * h - 1/6 * i - 1/6 * j - 1/6 *
k - 1/6 * l, Synt = ~1/4 * a + 1/4 * d + 1/4 * g + 1/4 *
j - 1/8 * b - 1/8 * c - 1/8 * e - 1/8 * f - 1/8 * h - 1/8 *
i - 1/8 * k - 1/8 * l, Lex = ~1/4 * b + 1/4 * e + 1/4 * h +
1/4 * k - 1/8 * a - 1/8 * c - 1/8 * d - 1/8 * f - 1/8 * g -
1/8 * i - 1/8 * j - 1/8 * l, Gram_x_Synt = ~1/8 * e + 1/8 *
f + 1/8 * k + 1/8 * l - 1/4 * d - 1/4 * j - 1/8 * b - 1/8 *
c - 1/8 * h - 1/8 * i + 1/4 * a + 1/4 * g, Gram_x_Lex = ~1/8 *
d + 1/8 * f + 1/8 * j + 1/8 * l - 1/4 * e - 1/4 * k - 1/8 *
a - 1/8 * c - 1/8 * g - 1/8 * i + 1/4 * b + 1/4 * h, Gram_x_Gen_x_Synt = ~1/4 *
h + 1/4 * i - 1/2 * g - 1/4 * k - 1/4 * l + 1/2 * j - 1/4 *
b - 1/4 * c + 1/2 * a + 1/4 * e + 1/4 * f - 1/2 * d, Gram_x_Gen_x_Lex = ~1/4 *
g + 1/4 * i - 1/2 * h - 1/4 * j - 1/4 * l + 1/2 * k - 1/4 *
a - 1/4 * c + 1/2 * b + 1/4 * d + 1/4 * f - 1/2 * e, levels = c("a",
"b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l"))
Hypothesis matrix (transposed):
Gram Gen Synt Lex Gram_x_Synt Gram_x_Lex Gram_x_Gen_x_Synt Gram_x_Gen_x_Lex
a 1/6 1/6 1/4 -1/8 1/4 -1/8 1/2 -1/4
b 1/6 1/6 -1/8 1/4 -1/8 1/4 -1/4 1/2
c 1/6 1/6 -1/8 -1/8 -1/8 -1/8 -1/4 -1/4
d -1/6 1/6 1/4 -1/8 -1/4 1/8 -1/2 1/4
e -1/6 1/6 -1/8 1/4 1/8 -1/4 1/4 -1/2
f -1/6 1/6 -1/8 -1/8 1/8 1/8 1/4 1/4
g 1/6 -1/6 1/4 -1/8 1/4 -1/8 -1/2 1/4
h 1/6 -1/6 -1/8 1/4 -1/8 1/4 1/4 -1/2
i 1/6 -1/6 -1/8 -1/8 -1/8 -1/8 1/4 1/4
j -1/6 -1/6 1/4 -1/8 -1/4 1/8 1/2 -1/4
k -1/6 -1/6 -1/8 1/4 1/8 -1/4 -1/4 1/2
l -1/6 -1/6 -1/8 -1/8 1/8 1/8 -1/4 -1/4
Contrast matrix:
Gram Gen Synt Lex Gram_x_Synt Gram_x_Lex Gram_x_Gen_x_Synt Gram_x_Gen_x_Lex
a 1/2 1/2 2/3 0 2/3 0 1/3 0
b 1/2 1/2 0 2/3 0 2/3 0 1/3
c 1/2 1/2 -2/3 -2/3 -2/3 -2/3 -1/3 -1/3
d -1/2 1/2 2/3 0 -2/3 0 -1/3 0
e -1/2 1/2 0 2/3 0 -2/3 0 -1/3
f -1/2 1/2 -2/3 -2/3 2/3 2/3 1/3 1/3
g 1/2 -1/2 2/3 0 2/3 0 -1/3 0
h 1/2 -1/2 0 2/3 0 2/3 0 -1/3
i 1/2 -1/2 -2/3 -2/3 -2/3 -2/3 1/3 1/3
j -1/2 -1/2 2/3 0 -2/3 0 1/3 0
k -1/2 -1/2 0 2/3 0 -2/3 0 1/3
l -1/2 -1/2 -2/3 -2/3 2/3 2/3 -1/3 -1/3 stats_freq = data.frame()
# regions = c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions = c("R3")
measure_types = c("gaze_duration", "go_past_time", "total_duration",
"FPReg", "RegIn_incl"
)
for (meth in methods) {
for (region in regions) {
for (meas in measure_types){
print(paste("Fitting model for:", meas, "in Region:", region))
if (meas %in% c("first_duration", "gaze_duration", "go_past_time", "total_duration")){
if (meth == "motr") {
model <- contr_motr %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]])) %>%
lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 + Gram | subj_id)")),
data = ., REML = F)
} else {
model <- contr_et %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]])) %>%
lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 + Gram | subj_id)")),
data = ., REML = F)
}
coefs <- summary(model)$coefficients
temp_results <- data.frame(
method = meth,
region = region,
measure = meas,
beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt",
"b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
bval = coefs[, "Estimate"],
pval = coefs[, "Pr(>|t|)"]
)
}else{
if (meth == "motr") {
model <- contr_motr %>% filter(!is.na(.data[[meas]])) %>%
glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 | subj_id)")),
data = ., family=binomial(link = "logit"))
} else{
model <- contr_et %>% filter(!is.na(.data[[meas]])) %>%
glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
(1 | item_id) + (1 | subj_id)")),
data = ., family=binomial(link = "logit"))
}
coefs <- summary(model)$coefficients
temp_results <- data.frame(
method = meth,
region = region,
measure = meas,
beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt",
"b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
bval = coefs[, "Estimate"],
pval = coefs[, "Pr(>|z|)"]
)
}
stats_freq = rbind(stats_freq, temp_results)
}
}
}
stats_freq <- stats_freq %>%
mutate(sig = case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
pval < 0.1 ~ ".",
TRUE ~ ""
))
# View(stats_freq)
# write.csv(stats_freq, "./stats/stats_motr_et_lmer.csv", row.names = FALSE)Fit Bayesian models
function for creating stan data format
createStanData <-function(d, dv,form){
subj <- as.integer(factor(d$subj_id))
N_subj <- length(unique(subj))
item <- as.integer(factor(d$item_id))
N_items <- length(unique(item))
X <- unname(model.matrix(form, d))
attr(X, which="assign") <- NULL
stanData <- list(N = nrow(X),
P = ncol(X),
n_u = ncol(X),
n_w = 3,
X = X,
Z_u = X,
Z_w = X[, 1:3, drop = FALSE], # only by-item random intercept, Gram, Gen
J = N_subj,
K = N_items,
dv = dv,
subj = subj,
item = item)
stanData
}# note: this chunk takes time to run ~ 1 hour for one region
# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
# measure_types <- c("go_past_time")
for (meth in methods) {
for (region in regions) {
for (meas in measure_types) {
print(paste("Fitting Bayesian model for:", meas, "in Region:", region))
if (meth == "motr"){
# Filter data for current region and non-missing measure
temp <- contr_motr %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]]))
} else {
temp <- contr_et %>%
filter(AOI_id == region) %>%
filter(!is.na(.data[[meas]]))
}
# binary dv
if (meas %in% c("FPReg", "RegIn_incl")) {
stan_data <- createStanData(
d = temp,
form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"),
dv = temp[[meas]]
)
stan_model_file <- "stan/Model_binary.stan"
} else {
# For other measures, use the default formulas and models
stan_data <- createStanData(
d = temp,
form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"),
dv = temp[[meas]]
)
stan_model_file <- "stan/Model_RT.stan"
}
# Fit model
stan_model <- stan(
file = stan_model_file,
data = stan_data,
iter = 4000,
chains = 4,
control = list(adapt_delta = 0.99)
)
# Save model output
model_save_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
saveRDS(stan_model, file = model_save_path)
}
}
}Examine fitted stan models
# change xx.rds to other models to check them
region <- "R3"
meas <- "go_past_time"
model_path <- paste0("models/et_", meas, "_", region, ".rds")
m1_gd <- readRDS(model_path)
summary(m1_gd)$summary
mean se_mean sd 2.5% 25% 50%
beta[1] 5.7137276 0.00116026 0.04449 5.6255388 5.68434020 5.713520936
beta[2] 0.1278191 0.00028141 0.02518 0.0767242 0.11149445 0.127758066
beta[3] -0.0110824 0.00022626 0.02377 -0.0582467 -0.02680162 -0.011386977
beta[4] -0.0512615 0.00064983 0.03962 -0.1301893 -0.07721791 -0.051252348
beta[5] -0.1106493 0.00063248 0.04011 -0.1865604 -0.13833594 -0.111422194
beta[6] 0.0117341 0.00022192 0.02214 -0.0312682 -0.00309903 0.011362633
beta[7] -0.0248344 0.00021342 0.02185 -0.0669826 -0.03937759 -0.024796391
beta[8] -0.0045650 0.00035250 0.03875 -0.0806718 -0.03032326 -0.004716087
beta[9] -0.0206183 0.00033778 0.03779 -0.0954384 -0.04559835 -0.020265754
sigma_e 0.3921495 0.00007697 0.00720 0.3785931 0.38721123 0.391947750
sigma_u[1] 0.2529664 0.00075530 0.03213 0.1990411 0.23037704 0.249860362
sigma_u[2] 0.1053344 0.00069806 0.03168 0.0397446 0.08587754 0.105457676
sigma_u[3] 0.0210309 0.00019291 0.01639 0.0008581 0.00809264 0.017342948
sigma_u[4] 0.0264723 0.00028287 0.01993 0.0011474 0.01047744 0.022516511
sigma_u[5] 0.0370721 0.00042782 0.02568 0.0017461 0.01621900 0.033231597
sigma_u[6] 0.0402110 0.00044760 0.02594 0.0019864 0.01902661 0.037557373
sigma_u[7] 0.0290649 0.00033137 0.02160 0.0011088 0.01168992 0.025014587
sigma_u[8] 0.0700658 0.00078836 0.04788 0.0032722 0.03151649 0.062843893
sigma_u[9] 0.0504653 0.00051471 0.03727 0.0022300 0.02067267 0.043085240
sigma_w[1] 0.1215831 0.00030786 0.01715 0.0912811 0.10935583 0.120383139
sigma_w[2] 0.0455099 0.00049511 0.02889 0.0019983 0.02207275 0.043196198
sigma_w[3] 0.0944788 0.00079417 0.03456 0.0181357 0.07367294 0.096009703
L_u[1,1] 1.0000000 NaN 0.00000 1.0000000 1.00000000 1.000000000
L_u[1,2] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,3] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,4] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[1,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,1] 0.1552419 0.00215168 0.19956 -0.2491587 0.02349424 0.160363298
L_u[2,2] 0.9665586 0.00056708 0.04296 0.8470914 0.95302364 0.983105894
L_u[2,3] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,4] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[2,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,1] -0.0359758 0.00215963 0.28366 -0.5744168 -0.23447634 -0.039973263
L_u[3,2] 0.0209408 0.00220376 0.28972 -0.5337836 -0.18367019 0.025472455
L_u[3,3] 0.9094959 0.00155353 0.08187 0.6979178 0.87061704 0.932317628
L_u[3,4] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[3,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,1] -0.0132876 0.00226170 0.27396 -0.5313123 -0.20401133 -0.015092069
L_u[4,2] 0.0077878 0.00223027 0.28129 -0.5460384 -0.19074487 0.009194853
L_u[4,3] -0.0089796 0.00237496 0.29254 -0.5614333 -0.22141558 -0.008826501
L_u[4,4] 0.8660699 0.00172079 0.09939 0.6193810 0.81233656 0.888783980
L_u[4,5] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[4,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,1] -0.0314604 0.00218045 0.25860 -0.5123615 -0.21454363 -0.034753994
L_u[5,2] -0.0077931 0.00252056 0.27874 -0.5429627 -0.20973779 -0.007226191
L_u[5,3] -0.0151788 0.00290837 0.29238 -0.5705059 -0.22303423 -0.019483430
L_u[5,4] -0.0109113 0.00301111 0.29182 -0.5716054 -0.21879207 -0.011878085
L_u[5,5] 0.8190122 0.00177359 0.11243 0.5472738 0.75386858 0.839161068
L_u[5,6] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[5,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[6,1] 0.1699310 0.00241785 0.26811 -0.3915943 -0.00623231 0.191516332
L_u[6,2] -0.1232831 0.00289328 0.27899 -0.6195553 -0.32823579 -0.136666671
L_u[6,3] -0.0056129 0.00279400 0.28560 -0.5533982 -0.20598338 -0.006156572
L_u[6,4] 0.0049108 0.00271092 0.28134 -0.5368213 -0.19549189 0.006578870
L_u[6,5] -0.0144534 0.00279867 0.28122 -0.5379807 -0.21668980 -0.015900686
L_u[6,6] 0.7407234 0.00212625 0.13238 0.4467269 0.65521301 0.755071546
L_u[6,7] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[6,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[6,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[7,1] 0.0225342 0.00231310 0.27254 -0.5055719 -0.16540892 0.020566509
L_u[7,2] -0.0173667 0.00249217 0.28622 -0.5641868 -0.22264554 -0.017735141
L_u[7,3] -0.0056213 0.00253197 0.29154 -0.5626096 -0.21212310 -0.008479741
L_u[7,4] -0.0194204 0.00249284 0.29218 -0.5691138 -0.22475292 -0.018512991
L_u[7,5] -0.0115889 0.00259252 0.29089 -0.5593560 -0.22242404 -0.018337900
L_u[7,6] -0.0505733 0.00261831 0.29493 -0.6122371 -0.26017809 -0.055786325
L_u[7,7] 0.6910385 0.00254748 0.14303 0.3766181 0.59996601 0.706481272
L_u[7,8] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[7,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[8,1] -0.0187178 0.00195627 0.26327 -0.5253724 -0.20407320 -0.021178999
L_u[8,2] 0.1322966 0.00264911 0.27673 -0.4278802 -0.05991167 0.144514676
L_u[8,3] -0.0101853 0.00282423 0.28970 -0.5544915 -0.21774771 -0.010900006
L_u[8,4] -0.0136163 0.00278354 0.28947 -0.5625898 -0.22088930 -0.012913273
L_u[8,5] 0.0316614 0.00282775 0.28592 -0.5346124 -0.16834414 0.037165430
L_u[8,6] -0.0517191 0.00290483 0.28517 -0.5878236 -0.25370805 -0.060174047
L_u[8,7] -0.0100569 0.00287979 0.29450 -0.5785394 -0.22076021 -0.007100980
L_u[8,8] 0.6259864 0.00233419 0.15127 0.3118438 0.52113413 0.634421109
L_u[8,9] 0.0000000 NaN 0.00000 0.0000000 0.00000000 0.000000000
L_u[9,1] 0.1099632 0.00222724 0.28671 -0.4739303 -0.08856047 0.124010155
L_u[9,2] -0.0142182 0.00250760 0.27879 -0.5484632 -0.21229502 -0.012128088
L_u[9,3] -0.0093962 0.00249653 0.28988 -0.5625705 -0.21389277 -0.008372280
L_u[9,4] 0.0020518 0.00242878 0.28715 -0.5547114 -0.19743645 0.003038669
L_u[9,5] -0.0098456 0.00233232 0.28797 -0.5568942 -0.21547740 -0.011699018
L_u[9,6] 0.0285579 0.00254097 0.28480 -0.5219290 -0.17183222 0.032007504
75% 97.5% n_eff Rhat
beta[1] 5.7436262 5.799604 1470 1.0003
beta[2] 0.1448759 0.176214 8008 0.9996
beta[3] 0.0046509 0.035814 11033 1.0001
beta[4] -0.0250399 0.026628 3717 1.0013
beta[5] -0.0836334 -0.030294 4021 1.0009
beta[6] 0.0267339 0.055618 9957 0.9996
beta[7] -0.0104658 0.018842 10482 1.0000
beta[8] 0.0210333 0.072646 12087 0.9997
beta[9] 0.0048688 0.054204 12517 0.9997
sigma_e 0.3968681 0.406687 8750 0.9997
sigma_u[1] 0.2721241 0.323789 1810 1.0018
sigma_u[2] 0.1258091 0.166874 2060 1.0017
sigma_u[3] 0.0303824 0.061450 7221 1.0000
sigma_u[4] 0.0382020 0.073839 4964 1.0011
sigma_u[5] 0.0539737 0.094945 3603 1.0012
sigma_u[6] 0.0577973 0.095719 3358 1.0008
sigma_u[7] 0.0421030 0.079998 4249 1.0008
sigma_u[8] 0.1013385 0.175856 3688 1.0002
sigma_u[9] 0.0726753 0.140210 5242 1.0000
sigma_w[1] 0.1323689 0.157345 3104 1.0003
sigma_w[2] 0.0645675 0.107832 3406 1.0002
sigma_w[3] 0.1175534 0.159070 1893 1.0013
L_u[1,1] 1.0000000 1.000000 NaN NaN
L_u[1,2] 0.0000000 0.000000 NaN NaN
L_u[1,3] 0.0000000 0.000000 NaN NaN
L_u[1,4] 0.0000000 0.000000 NaN NaN
L_u[1,5] 0.0000000 0.000000 NaN NaN
L_u[1,6] 0.0000000 0.000000 NaN NaN
L_u[1,7] 0.0000000 0.000000 NaN NaN
L_u[1,8] 0.0000000 0.000000 NaN NaN
L_u[1,9] 0.0000000 0.000000 NaN NaN
L_u[2,1] 0.2944338 0.530669 8602 0.9999
L_u[2,2] 0.9963201 0.999966 5740 0.9998
L_u[2,3] 0.0000000 0.000000 NaN NaN
L_u[2,4] 0.0000000 0.000000 NaN NaN
L_u[2,5] 0.0000000 0.000000 NaN NaN
L_u[2,6] 0.0000000 0.000000 NaN NaN
L_u[2,7] 0.0000000 0.000000 NaN NaN
L_u[2,8] 0.0000000 0.000000 NaN NaN
L_u[2,9] 0.0000000 0.000000 NaN NaN
L_u[3,1] 0.1630931 0.519153 17252 0.9997
L_u[3,2] 0.2290743 0.567851 17284 0.9999
L_u[3,3] 0.9724391 0.997542 2777 1.0012
L_u[3,4] 0.0000000 0.000000 NaN NaN
L_u[3,5] 0.0000000 0.000000 NaN NaN
L_u[3,6] 0.0000000 0.000000 NaN NaN
L_u[3,7] 0.0000000 0.000000 NaN NaN
L_u[3,8] 0.0000000 0.000000 NaN NaN
L_u[3,9] 0.0000000 0.000000 NaN NaN
L_u[4,1] 0.1756006 0.525587 14672 0.9997
L_u[4,2] 0.2095263 0.541930 15908 0.9998
L_u[4,3] 0.2014094 0.548018 15173 0.9998
L_u[4,4] 0.9417143 0.990184 3336 1.0014
L_u[4,5] 0.0000000 0.000000 NaN NaN
L_u[4,6] 0.0000000 0.000000 NaN NaN
L_u[4,7] 0.0000000 0.000000 NaN NaN
L_u[4,8] 0.0000000 0.000000 NaN NaN
L_u[4,9] 0.0000000 0.000000 NaN NaN
L_u[5,1] 0.1442983 0.482115 14066 0.9996
L_u[5,2] 0.1927303 0.520266 12229 0.9998
L_u[5,3] 0.1934844 0.552053 10107 0.9999
L_u[5,4] 0.1971026 0.546213 9393 0.9998
L_u[5,5] 0.9053139 0.975247 4019 1.0011
L_u[5,6] 0.0000000 0.000000 NaN NaN
L_u[5,7] 0.0000000 0.000000 NaN NaN
L_u[5,8] 0.0000000 0.000000 NaN NaN
L_u[5,9] 0.0000000 0.000000 NaN NaN
L_u[6,1] 0.3670603 0.638058 12296 0.9997
L_u[6,2] 0.0676310 0.456796 9298 0.9996
L_u[6,3] 0.1970512 0.538322 10449 0.9997
L_u[6,4] 0.2023692 0.545344 10770 0.9998
L_u[6,5] 0.1868490 0.533895 10097 1.0000
L_u[6,6] 0.8422310 0.945662 3876 1.0002
L_u[6,7] 0.0000000 0.000000 NaN NaN
L_u[6,8] 0.0000000 0.000000 NaN NaN
L_u[6,9] 0.0000000 0.000000 NaN NaN
L_u[7,1] 0.2159248 0.542306 13882 0.9998
L_u[7,2] 0.1828129 0.539232 13190 0.9997
L_u[7,3] 0.1994012 0.561794 13258 1.0000
L_u[7,4] 0.1858244 0.548850 13738 1.0004
L_u[7,5] 0.2011992 0.556384 12590 0.9999
L_u[7,6] 0.1578710 0.521280 12688 0.9996
L_u[7,7] 0.7992628 0.921234 3152 1.0006
L_u[7,8] 0.0000000 0.000000 NaN NaN
L_u[7,9] 0.0000000 0.000000 NaN NaN
L_u[8,1] 0.1654984 0.495741 18111 0.9999
L_u[8,2] 0.3357437 0.630349 10912 0.9996
L_u[8,3] 0.1925911 0.550140 10522 0.9999
L_u[8,4] 0.1899936 0.551566 10815 0.9997
L_u[8,5] 0.2381390 0.570667 10224 0.9996
L_u[8,6] 0.1454781 0.514621 9637 0.9998
L_u[8,7] 0.2009850 0.547276 10458 1.0004
L_u[8,8] 0.7395249 0.885437 4200 1.0004
L_u[8,9] 0.0000000 0.000000 NaN NaN
L_u[9,1] 0.3205461 0.624845 16572 0.9996
L_u[9,2] 0.1779917 0.523988 12361 0.9998
L_u[9,3] 0.1999384 0.543844 13482 0.9999
L_u[9,4] 0.2034316 0.545934 13978 0.9996
L_u[9,5] 0.1963436 0.541883 15245 0.9998
L_u[9,6] 0.2311829 0.568936 12563 0.9996
[ reached getOption("max.print") -- omitted 2782 rows ]
$c_summary
, , chains = chain:1
stats
parameter mean sd 2.5% 25% 50% 75%
beta[1] 5.71313764 0.043766 5.62739684 5.68300285 5.71254219 5.7431008
beta[2] 0.12791934 0.024950 0.07498792 0.11203706 0.12819914 0.1451459
beta[3] -0.01112213 0.023176 -0.05676759 -0.02666853 -0.01110475 0.0036782
beta[4] -0.04960346 0.039123 -0.12849671 -0.07545078 -0.04963835 -0.0238676
beta[5] -0.11217025 0.040447 -0.18641035 -0.14051785 -0.11348946 -0.0852989
beta[6] 0.01161017 0.022350 -0.03313501 -0.00338282 0.01135946 0.0266355
beta[7] -0.02447366 0.021900 -0.06722754 -0.03915830 -0.02470305 -0.0099852
beta[8] -0.00462087 0.037926 -0.07997108 -0.03014442 -0.00499958 0.0209451
beta[9] -0.02058520 0.036898 -0.09351357 -0.04481068 -0.02012587 0.0039586
sigma_e 0.39207544 0.007006 0.37895110 0.38729379 0.39194511 0.3966504
sigma_u[1] 0.25341894 0.031375 0.19717351 0.23161912 0.25047438 0.2727886
sigma_u[2] 0.10676070 0.032429 0.03871591 0.08727580 0.10591218 0.1271076
sigma_u[3] 0.02093646 0.016634 0.00092584 0.00786386 0.01684107 0.0301250
sigma_u[4] 0.02562677 0.019635 0.00120941 0.01004256 0.02144569 0.0369157
sigma_u[5] 0.03828180 0.026508 0.00150926 0.01655898 0.03422369 0.0559599
sigma_u[6] 0.04100796 0.026239 0.00217504 0.01966322 0.03848443 0.0588815
sigma_u[7] 0.02906976 0.022120 0.00086479 0.01126423 0.02495456 0.0421510
sigma_u[8] 0.07094983 0.047754 0.00369507 0.03224510 0.06500220 0.1023060
sigma_u[9] 0.05067299 0.037773 0.00215575 0.02084686 0.04349936 0.0727264
sigma_w[1] 0.12096332 0.017302 0.09114024 0.10883240 0.11981586 0.1318524
sigma_w[2] 0.04583392 0.029175 0.00206103 0.02178636 0.04395741 0.0649803
sigma_w[3] 0.09328626 0.034371 0.02019346 0.07267751 0.09430597 0.1161593
L_u[1,1] 1.00000000 0.000000 1.00000000 1.00000000 1.00000000 1.0000000
L_u[1,2] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,4] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[1,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,1] 0.15691221 0.198403 -0.24633552 0.02057949 0.16170754 0.2921020
L_u[2,2] 0.96650224 0.043690 0.83861601 0.95379683 0.98293134 0.9962636
L_u[2,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,4] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[2,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,1] -0.03815958 0.280535 -0.57738854 -0.23391465 -0.04326842 0.1580817
L_u[3,2] 0.02327923 0.287158 -0.54561289 -0.18194694 0.02724731 0.2305909
L_u[3,3] 0.91135128 0.079781 0.70215181 0.87428239 0.93284365 0.9724314
L_u[3,4] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[3,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,1] -0.01088570 0.274922 -0.52293787 -0.20278968 -0.01841786 0.1809470
L_u[4,2] 0.00237729 0.286602 -0.55596744 -0.20295404 0.00247701 0.2057367
L_u[4,3] -0.00613867 0.282614 -0.54812308 -0.20956123 -0.01044375 0.1962542
L_u[4,4] 0.86780109 0.096383 0.62852262 0.81271087 0.89242124 0.9401862
L_u[4,5] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[4,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,1] -0.03311159 0.263181 -0.52491811 -0.21878930 -0.04167821 0.1418992
L_u[5,2] -0.00799661 0.277738 -0.52568994 -0.21851840 -0.01131838 0.1935469
L_u[5,3] -0.01144692 0.287777 -0.54902437 -0.22284774 -0.01082654 0.2035603
L_u[5,4] -0.00584091 0.278193 -0.53964344 -0.20364515 -0.01745674 0.1892421
L_u[5,5] 0.82493006 0.108234 0.55566337 0.76283284 0.84416201 0.9079595
L_u[5,6] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[5,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[6,1] 0.16956210 0.267689 -0.37672018 -0.01220813 0.18580735 0.3721858
L_u[6,2] -0.12543769 0.282335 -0.64196700 -0.32807746 -0.13682650 0.0685792
L_u[6,3] -0.00459167 0.284918 -0.54725398 -0.20795335 -0.00874155 0.1976958
L_u[6,4] 0.00057064 0.278315 -0.52223829 -0.20129768 0.00809006 0.1982392
L_u[6,5] -0.01470836 0.281455 -0.52534619 -0.21710368 -0.02559055 0.1899161
L_u[6,6] 0.74015935 0.135720 0.44082532 0.65320809 0.75746274 0.8435301
L_u[6,7] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[6,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[6,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[7,1] 0.02223804 0.286393 -0.54361721 -0.17311508 0.01798013 0.2284809
L_u[7,2] -0.02273791 0.284678 -0.54475863 -0.23416284 -0.03320993 0.1806743
L_u[7,3] 0.00483081 0.290280 -0.54724821 -0.20213406 0.00165665 0.2087496
L_u[7,4] -0.00923222 0.286111 -0.54747484 -0.20349797 -0.00614642 0.1895690
L_u[7,5] -0.00262984 0.287465 -0.55387225 -0.20618379 -0.00880802 0.2087780
L_u[7,6] -0.05041789 0.296039 -0.61400879 -0.26902257 -0.04725425 0.1596983
L_u[7,7] 0.69089174 0.140711 0.39227860 0.59995325 0.70340152 0.7983615
L_u[7,8] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[7,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[8,1] -0.01285382 0.261531 -0.53211650 -0.18674808 -0.01671620 0.1590671
L_u[8,2] 0.12842828 0.273342 -0.42672633 -0.06064058 0.13308968 0.3284855
L_u[8,3] -0.01337892 0.293813 -0.56139709 -0.22236081 -0.01603426 0.1902357
L_u[8,4] -0.00745980 0.293162 -0.57102597 -0.22307292 -0.00792881 0.1930580
L_u[8,5] 0.03277845 0.284400 -0.53456405 -0.16355298 0.03928224 0.2337294
L_u[8,6] -0.06042370 0.280563 -0.56892506 -0.26402724 -0.06592505 0.1350431
L_u[8,7] -0.00677262 0.294891 -0.59025188 -0.21309697 0.00582087 0.2015930
L_u[8,8] 0.62771498 0.150523 0.32329845 0.51563223 0.63997883 0.7387466
L_u[8,9] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_u[9,1] 0.10686680 0.293106 -0.48405733 -0.09298402 0.11863457 0.3227717
L_u[9,2] -0.01084245 0.282857 -0.53992432 -0.21696212 -0.00391492 0.1835020
L_u[9,3] -0.00891006 0.291879 -0.54734544 -0.21832521 -0.01351539 0.2004057
L_u[9,4] 0.00069897 0.287099 -0.54085698 -0.20163435 -0.00395903 0.2065248
L_u[9,5] -0.01175079 0.284262 -0.55028206 -0.20846445 -0.01642432 0.1832899
L_u[9,6] 0.02509193 0.283468 -0.52129200 -0.17244766 0.02640681 0.2266400
L_u[9,7] -0.00357695 0.297996 -0.58317961 -0.21948714 0.00082669 0.2097211
L_u[9,8] -0.01473812 0.286922 -0.57422429 -0.21179191 -0.01993437 0.1876050
L_u[9,9] 0.54346818 0.162940 0.21563928 0.43077542 0.55047447 0.6643300
L_w[1,1] 1.00000000 0.000000 1.00000000 1.00000000 1.00000000 1.0000000
L_w[1,2] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_w[1,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_w[2,1] -0.30401238 0.327826 -0.81357081 -0.54842820 -0.34794190 -0.1036936
L_w[2,2] 0.88657918 0.118932 0.58143246 0.82790983 0.92729917 0.9779800
L_w[2,3] 0.00000000 0.000000 0.00000000 0.00000000 0.00000000 0.0000000
L_w[3,1] 0.09866434 0.284537 -0.49200055 -0.09506462 0.11150080 0.2962814
L_w[3,2] 0.06968991 0.416688 -0.74774712 -0.23383831 0.08445145 0.3885910
L_w[3,3] 0.84291921 0.142981 0.46238734 0.77594717 0.88659776 0.9503859
z_u[1,1] -0.58836305 0.281761 -1.16452379 -0.77433173 -0.59517008 -0.3933977
z_u[1,2] -0.19810587 0.760095 -1.73080437 -0.70680533 -0.20447592 0.2908181
z_u[1,3] 0.05451835 0.965841 -1.81830564 -0.63292057 0.06102908 0.7309231
z_u[1,4] -0.06290138 0.946548 -1.85754943 -0.71735266 -0.06254379 0.5815496
z_u[1,5] -0.02408568 0.988924 -2.00436622 -0.68003504 -0.02460154 0.6140643
z_u[1,6] -0.03081845 0.971008 -1.95086607 -0.65307566 -0.03438576 0.5670417
z_u[1,7] 0.00815684 0.978331 -1.86808703 -0.67652457 -0.00754877 0.6877695
z_u[1,8] -0.16442090 0.976689 -2.09053011 -0.85709358 -0.16786003 0.5090049
z_u[1,9] -0.12258556 0.980552 -1.99227901 -0.78296841 -0.13381940 0.5390079
z_u[2,1] -0.85130600 0.311091 -1.46355826 -1.05503372 -0.84438152 -0.6335456
z_u[2,2] 0.41276530 0.774793 -1.17670203 -0.07002896 0.41045513 0.9031758
z_u[2,3] -0.09570116 0.993574 -2.00761788 -0.80007892 -0.08334085 0.5836216
z_u[2,4] -0.09015756 0.943493 -1.90553870 -0.74319352 -0.09933205 0.5593682
z_u[2,5] 0.02767682 0.935284 -1.85522397 -0.63726149 0.03898061 0.6269704
z_u[2,6] -0.17627449 0.942760 -1.94560330 -0.80124044 -0.19876300 0.4672770
z_u[2,7] 0.01783762 0.956114 -1.88396448 -0.60129251 0.03179733 0.6150744
z_u[2,8] -0.07304053 0.957596 -1.88858379 -0.72882270 -0.06598202 0.5688490
z_u[2,9] 0.04326736 1.009585 -1.88559130 -0.65605537 0.02488998 0.7591116
z_u[3,1] 0.25445523 0.273022 -0.29203265 0.07002352 0.25122544 0.4317084
z_u[3,2] -0.13891772 0.777111 -1.66120185 -0.66336185 -0.14096350 0.3946285
z_u[3,3] 0.23215544 1.022291 -1.79390399 -0.47038038 0.22885645 0.9363247
z_u[3,4] 0.05659148 0.961902 -1.81698240 -0.56878359 0.05211163 0.7230671
z_u[3,5] -0.35135651 1.020955 -2.34443886 -1.01799987 -0.34544883 0.3181704
z_u[3,6] -0.27131848 0.982458 -2.24011411 -0.92586297 -0.27164009 0.4090866
z_u[3,7] 0.21244089 0.967088 -1.69067176 -0.44610883 0.20609954 0.8708612
z_u[3,8] -0.10754544 0.985513 -2.03641887 -0.78073117 -0.09562434 0.5852269
z_u[3,9] -0.10414296 0.996398 -2.04179143 -0.75452794 -0.10650734 0.5510401
z_u[4,1] 0.96171463 0.293592 0.38699449 0.76344241 0.96441645 1.1553424
z_u[4,2] 0.65782315 0.783633 -0.91940016 0.12884452 0.67974249 1.1961863
z_u[4,3] -0.16766907 0.967897 -2.09135456 -0.84537292 -0.18184419 0.5307259
stats
parameter 97.5%
beta[1] 5.8011956
beta[2] 0.1759481
beta[3] 0.0348597
beta[4] 0.0272101
beta[5] -0.0275612
beta[6] 0.0541748
beta[7] 0.0200544
beta[8] 0.0709304
beta[9] 0.0535641
sigma_e 0.4055103
sigma_u[1] 0.3225137
sigma_u[2] 0.1694784
sigma_u[3] 0.0617176
sigma_u[4] 0.0735970
sigma_u[5] 0.0972911
sigma_u[6] 0.0975369
sigma_u[7] 0.0812909
sigma_u[8] 0.1732239
sigma_u[9] 0.1417590
sigma_w[1] 0.1575509
sigma_w[2] 0.1074995
sigma_w[3] 0.1590697
L_u[1,1] 1.0000000
L_u[1,2] 0.0000000
L_u[1,3] 0.0000000
L_u[1,4] 0.0000000
L_u[1,5] 0.0000000
L_u[1,6] 0.0000000
L_u[1,7] 0.0000000
L_u[1,8] 0.0000000
L_u[1,9] 0.0000000
L_u[2,1] 0.5447230
L_u[2,2] 0.9999672
L_u[2,3] 0.0000000
L_u[2,4] 0.0000000
L_u[2,5] 0.0000000
L_u[2,6] 0.0000000
L_u[2,7] 0.0000000
L_u[2,8] 0.0000000
L_u[2,9] 0.0000000
L_u[3,1] 0.5158774
L_u[3,2] 0.5456324
L_u[3,3] 0.9978860
L_u[3,4] 0.0000000
L_u[3,5] 0.0000000
L_u[3,6] 0.0000000
L_u[3,7] 0.0000000
L_u[3,8] 0.0000000
L_u[3,9] 0.0000000
L_u[4,1] 0.5196079
L_u[4,2] 0.5413704
L_u[4,3] 0.5525111
L_u[4,4] 0.9898307
L_u[4,5] 0.0000000
L_u[4,6] 0.0000000
L_u[4,7] 0.0000000
L_u[4,8] 0.0000000
L_u[4,9] 0.0000000
L_u[5,1] 0.4876544
L_u[5,2] 0.5105310
L_u[5,3] 0.5324791
L_u[5,4] 0.5282536
L_u[5,5] 0.9744629
L_u[5,6] 0.0000000
L_u[5,7] 0.0000000
L_u[5,8] 0.0000000
L_u[5,9] 0.0000000
L_u[6,1] 0.6351133
L_u[6,2] 0.4436601
L_u[6,3] 0.5429731
L_u[6,4] 0.5391719
L_u[6,5] 0.5487482
L_u[6,6] 0.9428585
L_u[6,7] 0.0000000
L_u[6,8] 0.0000000
L_u[6,9] 0.0000000
L_u[7,1] 0.5562617
L_u[7,2] 0.5348565
L_u[7,3] 0.5514739
L_u[7,4] 0.5446926
L_u[7,5] 0.5476183
L_u[7,6] 0.5097937
L_u[7,7] 0.9153556
L_u[7,8] 0.0000000
L_u[7,9] 0.0000000
L_u[8,1] 0.4944285
L_u[8,2] 0.6159061
L_u[8,3] 0.5535389
L_u[8,4] 0.5561478
L_u[8,5] 0.5909508
L_u[8,6] 0.4867742
L_u[8,7] 0.5516640
L_u[8,8] 0.8887552
L_u[8,9] 0.0000000
L_u[9,1] 0.6442499
L_u[9,2] 0.5298522
L_u[9,3] 0.5422653
L_u[9,4] 0.5446919
L_u[9,5] 0.5491093
L_u[9,6] 0.5715091
L_u[9,7] 0.5583302
L_u[9,8] 0.5275687
L_u[9,9] 0.8357232
L_w[1,1] 1.0000000
L_w[1,2] 0.0000000
L_w[1,3] 0.0000000
L_w[2,1] 0.4571604
L_w[2,2] 0.9998347
L_w[2,3] 0.0000000
L_w[3,1] 0.6215726
L_w[3,2] 0.7886976
L_w[3,3] 0.9956475
z_u[1,1] -0.0535598
z_u[1,2] 1.3277736
z_u[1,3] 1.8427887
z_u[1,4] 1.7374492
z_u[1,5] 1.9529460
z_u[1,6] 1.9636886
z_u[1,7] 1.8837358
z_u[1,8] 1.7373880
z_u[1,9] 1.8479007
z_u[2,1] -0.2787172
z_u[2,2] 1.9232673
z_u[2,3] 1.8763920
z_u[2,4] 1.7435384
z_u[2,5] 1.8946007
z_u[2,6] 1.6691390
z_u[2,7] 1.9405676
z_u[2,8] 1.7928960
z_u[2,9] 2.0283817
z_u[3,1] 0.8041124
z_u[3,2] 1.3496413
z_u[3,3] 2.1776256
z_u[3,4] 1.9222296
z_u[3,5] 1.7465426
z_u[3,6] 1.6077870
z_u[3,7] 2.1181724
z_u[3,8] 1.8141622
z_u[3,9] 1.8201266
z_u[4,1] 1.5651933
z_u[4,2] 2.1107694
z_u[4,3] 1.6024478
[ reached getOption("max.print") -- omitted 2740 row(s) and 3 matrix slice(s) ]# check params
summary(m1_gd, pars = c('beta[1]', 'beta[2]', 'beta[3]', 'beta[4]', 'beta[5]', 'beta[6]', 'beta[7]', 'beta[8]', 'beta[9]'))$summary
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
beta[1] 5.713728 0.0011603 0.04449 5.62554 5.684340 5.713521 5.743626 5.79960 1470 1.0003
beta[2] 0.127819 0.0002814 0.02518 0.07672 0.111494 0.127758 0.144876 0.17621 8008 0.9996
beta[3] -0.011082 0.0002263 0.02377 -0.05825 -0.026802 -0.011387 0.004651 0.03581 11033 1.0001
beta[4] -0.051262 0.0006498 0.03962 -0.13019 -0.077218 -0.051252 -0.025040 0.02663 3717 1.0013
beta[5] -0.110649 0.0006325 0.04011 -0.18656 -0.138336 -0.111422 -0.083633 -0.03029 4021 1.0009
beta[6] 0.011734 0.0002219 0.02214 -0.03127 -0.003099 0.011363 0.026734 0.05562 9957 0.9996
beta[7] -0.024834 0.0002134 0.02185 -0.06698 -0.039378 -0.024796 -0.010466 0.01884 10482 1.0000
beta[8] -0.004565 0.0003525 0.03875 -0.08067 -0.030323 -0.004716 0.021033 0.07265 12087 0.9997
beta[9] -0.020618 0.0003378 0.03779 -0.09544 -0.045598 -0.020266 0.004869 0.05420 12517 0.9997
$c_summary
, , chains = chain:1
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.713138 0.04377 5.62740 5.683003 5.71254 5.743101 5.80120
beta[2] 0.127919 0.02495 0.07499 0.112037 0.12820 0.145146 0.17595
beta[3] -0.011122 0.02318 -0.05677 -0.026669 -0.01110 0.003678 0.03486
beta[4] -0.049603 0.03912 -0.12850 -0.075451 -0.04964 -0.023868 0.02721
beta[5] -0.112170 0.04045 -0.18641 -0.140518 -0.11349 -0.085299 -0.02756
beta[6] 0.011610 0.02235 -0.03314 -0.003383 0.01136 0.026635 0.05417
beta[7] -0.024474 0.02190 -0.06723 -0.039158 -0.02470 -0.009985 0.02005
beta[8] -0.004621 0.03793 -0.07997 -0.030144 -0.00500 0.020945 0.07093
beta[9] -0.020585 0.03690 -0.09351 -0.044811 -0.02013 0.003959 0.05356
, , chains = chain:2
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.715037 0.04484 5.62636 5.685916 5.715053 5.745564 5.80027
beta[2] 0.127851 0.02466 0.07754 0.112097 0.127127 0.144226 0.17510
beta[3] -0.010676 0.02410 -0.05826 -0.025909 -0.011212 0.004843 0.03648
beta[4] -0.051229 0.03913 -0.12902 -0.076160 -0.051931 -0.025282 0.02530
beta[5] -0.111027 0.03969 -0.18941 -0.138046 -0.111284 -0.083035 -0.03563
beta[6] 0.011793 0.02201 -0.03023 -0.003705 0.011121 0.026480 0.05697
beta[7] -0.024973 0.02246 -0.06628 -0.039961 -0.025135 -0.010662 0.02017
beta[8] -0.004396 0.03937 -0.08104 -0.030887 -0.003905 0.021598 0.07505
beta[9] -0.020592 0.03804 -0.09312 -0.046980 -0.020486 0.005699 0.05219
, , chains = chain:3
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.71471 0.04561 5.62498 5.686380 5.714693 5.745015 5.80040
beta[2] 0.12815 0.02577 0.07499 0.112058 0.128429 0.145906 0.17634
beta[3] -0.01096 0.02410 -0.05808 -0.027102 -0.011103 0.005638 0.03571
beta[4] -0.05028 0.04059 -0.12881 -0.077789 -0.050275 -0.022809 0.02851
beta[5] -0.11035 0.03992 -0.18650 -0.136889 -0.110758 -0.084419 -0.02947
beta[6] 0.01177 0.02219 -0.03032 -0.003241 0.011586 0.026975 0.05532
beta[7] -0.02511 0.02162 -0.06740 -0.039077 -0.024977 -0.010970 0.01809
beta[8] -0.00539 0.03962 -0.08210 -0.032134 -0.006221 0.021030 0.07272
beta[9] -0.02108 0.03804 -0.09897 -0.046205 -0.020819 0.004461 0.05202
, , chains = chain:4
stats
parameter mean sd 2.5% 25% 50% 75% 97.5%
beta[1] 5.712026 0.04368 5.62513 5.682875 5.712404 5.740672 5.79525
beta[2] 0.127359 0.02535 0.07959 0.109347 0.127041 0.143869 0.17762
beta[3] -0.011570 0.02368 -0.05916 -0.027653 -0.011936 0.004601 0.03554
beta[4] -0.053935 0.03951 -0.13240 -0.080639 -0.053332 -0.027947 0.02386
beta[5] -0.109051 0.04033 -0.18296 -0.137600 -0.109879 -0.081789 -0.02636
beta[6] 0.011759 0.02205 -0.03076 -0.002476 0.011476 0.026602 0.05549
beta[7] -0.024785 0.02142 -0.06650 -0.039542 -0.024316 -0.010204 0.01554
beta[8] -0.003853 0.03808 -0.07760 -0.028500 -0.004147 0.020882 0.07188
beta[9] -0.020212 0.03820 -0.09636 -0.044417 -0.019729 0.005767 0.05676# check convergence
traceplot(m1_gd, pars = c("beta"))
# check predicts --> posterior parameter distr.
y_posterior <- extract(m1_gd)
y_posterior$beta[,1] #intercept [1] 5.751 5.751 5.679 5.755 5.769 5.735 5.704 5.784 5.787 5.658 5.768 5.707 5.673 5.663 5.671 5.740
[17] 5.708 5.772 5.714 5.732 5.699 5.704 5.769 5.763 5.655 5.689 5.732 5.714 5.739 5.706 5.723 5.655
[33] 5.773 5.697 5.695 5.676 5.726 5.752 5.669 5.665 5.715 5.734 5.594 5.739 5.707 5.711 5.747 5.695
[49] 5.740 5.748 5.720 5.726 5.722 5.749 5.761 5.679 5.722 5.667 5.724 5.655 5.741 5.705 5.757 5.717
[65] 5.704 5.630 5.604 5.754 5.710 5.710 5.645 5.677 5.760 5.705 5.679 5.775 5.707 5.771 5.724 5.699
[81] 5.762 5.743 5.684 5.704 5.631 5.763 5.689 5.667 5.705 5.743 5.741 5.731 5.736 5.700 5.652 5.788
[97] 5.747 5.753 5.703 5.705 5.803 5.691 5.667 5.658 5.699 5.650 5.733 5.702 5.685 5.748 5.788 5.733
[113] 5.671 5.716 5.665 5.724 5.709 5.694 5.739 5.736 5.752 5.745 5.644 5.749 5.691 5.700 5.637 5.672
[129] 5.735 5.697 5.701 5.713 5.712 5.759 5.709 5.651 5.697 5.757 5.691 5.743 5.705 5.731 5.699 5.659
[145] 5.714 5.696 5.687 5.697 5.705 5.783 5.723 5.702 5.732 5.748 5.692 5.734 5.756 5.803 5.702 5.774
[161] 5.805 5.757 5.728 5.696 5.702 5.677 5.678 5.686 5.676 5.700 5.681 5.716 5.703 5.714 5.737 5.685
[177] 5.677 5.697 5.739 5.731 5.734 5.808 5.703 5.735 5.809 5.677 5.748 5.722 5.651 5.803 5.726 5.656
[193] 5.668 5.727 5.767 5.820 5.742 5.733 5.624 5.719 5.668 5.663 5.769 5.761 5.768 5.790 5.709 5.692
[209] 5.684 5.789 5.675 5.734 5.668 5.698 5.721 5.686 5.793 5.657 5.696 5.739 5.655 5.740 5.690 5.656
[225] 5.727 5.694 5.718 5.704 5.694 5.698 5.722 5.733 5.665 5.726 5.742 5.704 5.684 5.681 5.736 5.643
[241] 5.702 5.752 5.693 5.717 5.687 5.750 5.740 5.750 5.760 5.651 5.747 5.721 5.739 5.757 5.755 5.676
[257] 5.730 5.767 5.654 5.786 5.701 5.735 5.664 5.691 5.742 5.741 5.699 5.715 5.673 5.711 5.714 5.787
[273] 5.807 5.709 5.694 5.842 5.723 5.741 5.762 5.648 5.668 5.674 5.685 5.701 5.653 5.717 5.613 5.751
[289] 5.754 5.691 5.694 5.757 5.763 5.621 5.683 5.728 5.643 5.679 5.681 5.719 5.668 5.743 5.723 5.684
[305] 5.711 5.777 5.755 5.723 5.735 5.721 5.724 5.656 5.683 5.646 5.798 5.765 5.776 5.683 5.778 5.698
[321] 5.697 5.607 5.779 5.649 5.703 5.665 5.632 5.686 5.782 5.703 5.715 5.686 5.715 5.716 5.715 5.738
[337] 5.719 5.680 5.717 5.675 5.699 5.776 5.718 5.745 5.584 5.757 5.706 5.716 5.701 5.691 5.679 5.742
[353] 5.662 5.680 5.717 5.769 5.698 5.750 5.701 5.740 5.835 5.702 5.717 5.730 5.754 5.651 5.740 5.685
[369] 5.698 5.673 5.709 5.675 5.798 5.700 5.682 5.671 5.628 5.734 5.642 5.714 5.754 5.661 5.700 5.754
[385] 5.726 5.818 5.761 5.733 5.696 5.712 5.654 5.701 5.782 5.668 5.689 5.734 5.737 5.750 5.796 5.695
[401] 5.775 5.664 5.753 5.761 5.703 5.711 5.747 5.696 5.738 5.644 5.676 5.689 5.727 5.719 5.769 5.716
[417] 5.719 5.683 5.664 5.727 5.671 5.695 5.758 5.736 5.754 5.738 5.704 5.629 5.729 5.677 5.677 5.691
[433] 5.696 5.781 5.652 5.670 5.646 5.724 5.688 5.685 5.742 5.755 5.734 5.790 5.688 5.735 5.706 5.720
[449] 5.751 5.629 5.732 5.655 5.798 5.718 5.660 5.735 5.753 5.739 5.708 5.710 5.746 5.672 5.703 5.700
[465] 5.728 5.677 5.691 5.690 5.792 5.754 5.733 5.676 5.698 5.791 5.704 5.729 5.696 5.640 5.655 5.759
[481] 5.676 5.684 5.670 5.719 5.699 5.712 5.678 5.649 5.715 5.677 5.733 5.692 5.664 5.755 5.664 5.624
[497] 5.738 5.682 5.678 5.624 5.708 5.716 5.687 5.719 5.732 5.697 5.618 5.740 5.770 5.670 5.699 5.716
[513] 5.699 5.743 5.741 5.745 5.757 5.671 5.721 5.709 5.737 5.649 5.777 5.665 5.687 5.709 5.688 5.709
[529] 5.720 5.783 5.791 5.735 5.709 5.606 5.780 5.724 5.672 5.738 5.668 5.676 5.695 5.692 5.714 5.851
[545] 5.772 5.764 5.686 5.673 5.753 5.678 5.752 5.727 5.714 5.683 5.635 5.727 5.673 5.713 5.786 5.681
[561] 5.618 5.773 5.726 5.705 5.743 5.740 5.644 5.744 5.706 5.704 5.743 5.676 5.735 5.792 5.766 5.746
[577] 5.754 5.616 5.759 5.680 5.712 5.667 5.681 5.681 5.782 5.725 5.698 5.709 5.696 5.762 5.713 5.692
[593] 5.675 5.717 5.709 5.690 5.673 5.694 5.693 5.652 5.685 5.708 5.704 5.696 5.686 5.747 5.678 5.687
[609] 5.737 5.700 5.752 5.715 5.756 5.650 5.767 5.747 5.707 5.747 5.712 5.690 5.719 5.731 5.671 5.708
[625] 5.695 5.669 5.784 5.675 5.715 5.729 5.657 5.647 5.720 5.735 5.708 5.707 5.681 5.734 5.644 5.695
[641] 5.673 5.708 5.685 5.722 5.701 5.671 5.759 5.688 5.688 5.698 5.766 5.801 5.710 5.701 5.775 5.716
[657] 5.710 5.738 5.727 5.726 5.652 5.737 5.789 5.734 5.713 5.691 5.753 5.771 5.627 5.575 5.724 5.733
[673] 5.697 5.707 5.762 5.720 5.696 5.656 5.769 5.728 5.689 5.765 5.690 5.762 5.694 5.673 5.691 5.706
[689] 5.676 5.774 5.711 5.722 5.693 5.731 5.799 5.654 5.702 5.681 5.744 5.657 5.727 5.688 5.759 5.762
[705] 5.753 5.718 5.816 5.624 5.739 5.647 5.682 5.729 5.713 5.723 5.673 5.770 5.618 5.725 5.746 5.717
[721] 5.671 5.693 5.636 5.608 5.719 5.700 5.740 5.737 5.699 5.749 5.668 5.724 5.755 5.816 5.736 5.754
[737] 5.712 5.721 5.715 5.667 5.757 5.704 5.718 5.738 5.778 5.693 5.772 5.668 5.721 5.703 5.708 5.675
[753] 5.676 5.733 5.752 5.627 5.677 5.706 5.741 5.715 5.754 5.719 5.745 5.666 5.674 5.699 5.670 5.673
[769] 5.740 5.689 5.661 5.709 5.681 5.715 5.730 5.675 5.713 5.770 5.727 5.650 5.722 5.720 5.757 5.710
[785] 5.718 5.732 5.701 5.717 5.768 5.649 5.703 5.683 5.706 5.743 5.692 5.675 5.690 5.694 5.725 5.694
[801] 5.710 5.731 5.778 5.720 5.781 5.727 5.709 5.732 5.694 5.740 5.658 5.744 5.745 5.738 5.820 5.699
[817] 5.697 5.676 5.670 5.749 5.701 5.777 5.749 5.731 5.604 5.623 5.731 5.743 5.688 5.659 5.710 5.760
[833] 5.758 5.765 5.713 5.711 5.636 5.731 5.652 5.631 5.664 5.640 5.756 5.658 5.763 5.627 5.637 5.711
[849] 5.669 5.690 5.746 5.728 5.711 5.738 5.702 5.707 5.762 5.746 5.717 5.634 5.770 5.627 5.720 5.649
[865] 5.748 5.770 5.760 5.765 5.760 5.799 5.688 5.677 5.757 5.747 5.687 5.647 5.731 5.619 5.645 5.758
[881] 5.688 5.728 5.712 5.699 5.676 5.769 5.687 5.745 5.660 5.758 5.737 5.660 5.709 5.698 5.737 5.678
[897] 5.785 5.707 5.791 5.746 5.683 5.663 5.648 5.708 5.723 5.675 5.704 5.709 5.701 5.677 5.652 5.705
[913] 5.753 5.768 5.703 5.705 5.707 5.832 5.716 5.692 5.727 5.713 5.809 5.682 5.692 5.705 5.699 5.682
[929] 5.701 5.624 5.671 5.684 5.799 5.660 5.656 5.656 5.788 5.819 5.693 5.758 5.724 5.723 5.714 5.637
[945] 5.755 5.716 5.652 5.770 5.752 5.683 5.708 5.695 5.710 5.722 5.692 5.690 5.719 5.711 5.766 5.749
[961] 5.779 5.692 5.726 5.731 5.746 5.694 5.766 5.753 5.675 5.764 5.640 5.672 5.659 5.658 5.777 5.690
[977] 5.672 5.740 5.713 5.749 5.698 5.724 5.753 5.718 5.680 5.768 5.704 5.662 5.681 5.752 5.730 5.758
[993] 5.661 5.747 5.744 5.603 5.741 5.778 5.674 5.789
[ reached getOption("max.print") -- omitted 7000 entries ]y_posterior$beta[,2] #Gram [1] 0.08617 0.14450 0.14781 0.12892 0.13129 0.13293 0.13507 0.14327 0.14966 0.14901 0.15392 0.14168
[13] 0.14565 0.12623 0.15916 0.10220 0.11038 0.14037 0.13081 0.14557 0.14672 0.13640 0.09500 0.17139
[25] 0.16874 0.15347 0.14150 0.14487 0.13399 0.16667 0.12961 0.09932 0.10611 0.11766 0.13663 0.13142
[37] 0.13723 0.07287 0.12577 0.06899 0.08543 0.12287 0.14221 0.14713 0.14097 0.07892 0.15540 0.11159
[49] 0.11691 0.10813 0.11586 0.14688 0.13697 0.13043 0.15569 0.14091 0.07121 0.12177 0.13269 0.10608
[61] 0.14402 0.12714 0.14093 0.14937 0.11413 0.12640 0.09119 0.13837 0.11502 0.10160 0.11414 0.07872
[73] 0.12658 0.12101 0.12752 0.11559 0.15402 0.14726 0.11283 0.13488 0.13121 0.18106 0.11671 0.15492
[85] 0.13950 0.12340 0.08882 0.13593 0.16429 0.14245 0.13861 0.12153 0.09980 0.10797 0.09945 0.12834
[97] 0.15143 0.12603 0.07650 0.13247 0.09456 0.15212 0.13793 0.13436 0.09781 0.15148 0.16614 0.11385
[109] 0.13221 0.15689 0.09666 0.11132 0.15233 0.10938 0.09012 0.09036 0.10308 0.14140 0.09856 0.13506
[121] 0.11320 0.11403 0.15441 0.10683 0.11736 0.14178 0.14920 0.12592 0.12665 0.11999 0.16340 0.15276
[133] 0.11877 0.12594 0.15326 0.13985 0.16150 0.13362 0.08675 0.13806 0.11504 0.15282 0.08628 0.12197
[145] 0.12630 0.12706 0.09020 0.13931 0.11416 0.09610 0.11782 0.13046 0.12236 0.10923 0.12507 0.10658
[157] 0.10899 0.10802 0.14134 0.12987 0.12139 0.13758 0.16275 0.14353 0.12167 0.14724 0.10874 0.10913
[169] 0.12759 0.11972 0.12935 0.09372 0.10615 0.11235 0.14458 0.10970 0.07592 0.10678 0.15926 0.13222
[181] 0.09524 0.13321 0.14408 0.17678 0.13372 0.13564 0.17511 0.16764 0.12833 0.13570 0.10194 0.08344
[193] 0.14541 0.05377 0.15002 0.14793 0.12174 0.15278 0.09622 0.12525 0.13503 0.11550 0.14684 0.15213
[205] 0.10659 0.11622 0.11635 0.11295 0.16448 0.10423 0.12234 0.12843 0.14249 0.10947 0.14683 0.11571
[217] 0.13161 0.13919 0.12992 0.13853 0.14603 0.13049 0.07034 0.10650 0.08632 0.14786 0.12386 0.17493
[229] 0.15394 0.10624 0.14887 0.10067 0.12072 0.11424 0.15735 0.16042 0.13650 0.16613 0.17444 0.12783
[241] 0.14970 0.14304 0.11044 0.15157 0.15409 0.12360 0.11551 0.14503 0.12144 0.15400 0.12469 0.14487
[253] 0.13745 0.12516 0.15474 0.13063 0.13175 0.12857 0.12539 0.11606 0.12027 0.19170 0.14063 0.08966
[265] 0.15662 0.18473 0.08527 0.15371 0.09278 0.13630 0.10137 0.16206 0.11499 0.10935 0.11129 0.13247
[277] 0.09457 0.10138 0.12166 0.12270 0.14339 0.12127 0.11183 0.10896 0.13236 0.10664 0.15681 0.13962
[289] 0.13195 0.11184 0.09870 0.13971 0.07197 0.16672 0.12538 0.16240 0.10174 0.09411 0.09811 0.13354
[301] 0.12356 0.13345 0.13153 0.11092 0.14653 0.11313 0.11546 0.13909 0.09056 0.12839 0.12640 0.09912
[313] 0.15115 0.19916 0.14901 0.11999 0.11155 0.11490 0.13676 0.09680 0.12861 0.07439 0.10428 0.13824
[325] 0.14970 0.10536 0.14174 0.10326 0.12899 0.14187 0.14581 0.15519 0.11016 0.09563 0.10252 0.13397
[337] 0.16051 0.12326 0.11764 0.14207 0.12551 0.11481 0.15527 0.10294 0.06382 0.15177 0.11655 0.14535
[349] 0.11328 0.14300 0.14893 0.14658 0.14335 0.12075 0.11869 0.09880 0.13223 0.15161 0.12589 0.19733
[361] 0.12428 0.13698 0.13464 0.14514 0.10326 0.10642 0.14590 0.15283 0.15162 0.10011 0.12288 0.16207
[373] 0.11674 0.13134 0.09859 0.13174 0.10200 0.10511 0.11659 0.12234 0.15189 0.12752 0.12517 0.12137
[385] 0.12267 0.04446 0.11596 0.13643 0.15836 0.14658 0.10246 0.16870 0.18755 0.16820 0.10961 0.18075
[397] 0.11354 0.07171 0.09722 0.17022 0.11216 0.16280 0.15870 0.20582 0.14103 0.14484 0.17494 0.13771
[409] 0.12974 0.15814 0.14417 0.14738 0.14563 0.08168 0.11137 0.15177 0.12507 0.11288 0.16363 0.10223
[421] 0.14722 0.11968 0.15899 0.12449 0.13869 0.09205 0.13671 0.15691 0.11568 0.14063 0.12028 0.12888
[433] 0.11979 0.06626 0.14543 0.08626 0.09647 0.07153 0.13870 0.13193 0.14451 0.14357 0.08044 0.16551
[445] 0.13039 0.15365 0.08732 0.10992 0.12014 0.11644 0.14069 0.12645 0.14660 0.09043 0.09341 0.14937
[457] 0.11532 0.14833 0.09702 0.12638 0.12350 0.11720 0.14513 0.11679 0.11915 0.12738 0.09644 0.13432
[469] 0.10587 0.13283 0.10026 0.17041 0.11957 0.13881 0.12078 0.12541 0.11916 0.16420 0.09504 0.15381
[481] 0.15325 0.11999 0.16943 0.08005 0.13699 0.08099 0.13111 0.06213 0.12644 0.10711 0.10377 0.16842
[493] 0.10034 0.14969 0.12705 0.14769 0.15639 0.12379 0.10424 0.08496 0.13809 0.18324 0.13765 0.13690
[505] 0.08981 0.09890 0.11781 0.11738 0.10540 0.09849 0.17603 0.13824 0.14696 0.18071 0.14536 0.12971
[517] 0.12861 0.11966 0.11337 0.14504 0.09835 0.15846 0.09273 0.14971 0.17260 0.08591 0.13329 0.14821
[529] 0.18201 0.15028 0.11969 0.17040 0.13395 0.13105 0.13440 0.15633 0.13406 0.04336 0.13434 0.12383
[541] 0.14595 0.15120 0.13406 0.12190 0.10521 0.11391 0.14633 0.17315 0.14189 0.10951 0.16390 0.20014
[553] 0.15735 0.12947 0.14806 0.11584 0.10904 0.11258 0.14033 0.17200 0.11461 0.13188 0.11179 0.09028
[565] 0.13988 0.11073 0.12052 0.15197 0.11854 0.14750 0.13312 0.16275 0.14386 0.13822 0.12464 0.12385
[577] 0.14302 0.10609 0.15688 0.11489 0.10273 0.11644 0.07321 0.13547 0.12621 0.13460 0.18187 0.15457
[589] 0.08827 0.14831 0.13677 0.15855 0.13647 0.06721 0.10787 0.13113 0.12921 0.13534 0.11620 0.09270
[601] 0.09484 0.09778 0.12848 0.14979 0.16408 0.11892 0.14428 0.12656 0.11207 0.10928 0.14767 0.08120
[613] 0.12933 0.08151 0.13366 0.13624 0.18558 0.14420 0.14177 0.12611 0.06975 0.15571 0.13356 0.12182
[625] 0.09578 0.11959 0.15866 0.08269 0.14066 0.10946 0.17298 0.10976 0.11781 0.13868 0.05977 0.13679
[637] 0.12436 0.07208 0.13179 0.08501 0.11879 0.11218 0.13005 0.14813 0.11696 0.13584 0.11975 0.06660
[649] 0.16091 0.11411 0.12108 0.13884 0.11474 0.12107 0.17435 0.12664 0.18112 0.13733 0.12864 0.16557
[661] 0.09005 0.10089 0.11636 0.11584 0.12421 0.12055 0.13789 0.14543 0.10242 0.07911 0.09262 0.12197
[673] 0.16440 0.14262 0.12237 0.11486 0.12937 0.14811 0.14572 0.14774 0.14459 0.12204 0.11245 0.14830
[685] 0.09418 0.12415 0.12969 0.03976 0.13366 0.09318 0.18340 0.13630 0.13642 0.13825 0.12225 0.13120
[697] 0.06837 0.11119 0.13583 0.15370 0.11017 0.13900 0.14234 0.12275 0.13802 0.12296 0.14886 0.13570
[709] 0.11289 0.10159 0.16925 0.11048 0.12514 0.09460 0.14195 0.11614 0.13376 0.11309 0.08897 0.14748
[721] 0.12434 0.12826 0.10744 0.15766 0.10934 0.14036 0.16213 0.08833 0.08198 0.18223 0.11762 0.13460
[733] 0.14590 0.12241 0.09997 0.13471 0.15441 0.15375 0.12639 0.14780 0.12179 0.16359 0.09880 0.15218
[745] 0.15039 0.14517 0.14172 0.17587 0.12947 0.13115 0.12604 0.10719 0.12735 0.12851 0.11255 0.12703
[757] 0.13626 0.10706 0.13023 0.11124 0.15471 0.12989 0.11844 0.11613 0.12551 0.13302 0.16780 0.13311
[769] 0.11801 0.12862 0.13812 0.12966 0.12752 0.14380 0.10379 0.09667 0.12599 0.13431 0.12687 0.11303
[781] 0.12263 0.09326 0.10503 0.14186 0.13954 0.14169 0.14216 0.19075 0.12190 0.13115 0.11137 0.10626
[793] 0.07689 0.07665 0.14223 0.11591 0.14908 0.13715 0.10665 0.15157 0.10092 0.16448 0.14414 0.10579
[805] 0.14298 0.13891 0.13119 0.12187 0.16606 0.12191 0.13380 0.15242 0.14120 0.15015 0.12004 0.07321
[817] 0.14435 0.18583 0.09775 0.13144 0.10317 0.10751 0.10747 0.12284 0.09342 0.11387 0.10395 0.13351
[829] 0.18951 0.11376 0.10294 0.12265 0.15853 0.13155 0.14616 0.15013 0.09802 0.12476 0.14579 0.10173
[841] 0.09113 0.10351 0.12155 0.11525 0.11997 0.12907 0.13051 0.16481 0.14488 0.10562 0.13479 0.13406
[853] 0.11385 0.14188 0.16336 0.09901 0.11036 0.13098 0.14489 0.11368 0.10775 0.10988 0.17271 0.14885
[865] 0.14082 0.11923 0.13267 0.11739 0.14694 0.12680 0.15773 0.08823 0.14693 0.14772 0.10690 0.11177
[877] 0.14767 0.12014 0.08837 0.14243 0.12652 0.13006 0.11516 0.14220 0.12479 0.12474 0.13651 0.17067
[889] 0.10511 0.10579 0.14242 0.07305 0.15001 0.17609 0.13973 0.11461 0.14059 0.09798 0.15346 0.11536
[901] 0.10475 0.10956 0.15445 0.13200 0.10959 0.13826 0.11600 0.07504 0.13317 0.14467 0.15274 0.07781
[913] 0.12626 0.13610 0.16437 0.11658 0.12503 0.11983 0.11008 0.11812 0.16014 0.11373 0.15979 0.16546
[925] 0.15146 0.09879 0.07878 0.16929 0.07895 0.13191 0.14494 0.15776 0.13905 0.11267 0.10646 0.14932
[937] 0.15015 0.15001 0.06972 0.16959 0.09255 0.09434 0.10668 0.14737 0.14571 0.13768 0.10172 0.13162
[949] 0.12990 0.12523 0.14373 0.15639 0.14883 0.10322 0.10213 0.14080 0.13515 0.13795 0.16567 0.13562
[961] 0.14240 0.11704 0.13071 0.10481 0.13580 0.08970 0.14595 0.12253 0.12586 0.16377 0.14675 0.15883
[973] 0.10412 0.10696 0.07533 0.13344 0.06805 0.15634 0.11425 0.12240 0.13767 0.10120 0.14719 0.12186
[985] 0.12799 0.16315 0.06677 0.11491 0.14141 0.17970 0.07001 0.14943 0.11737 0.16799 0.08687 0.06966
[997] 0.14371 0.16850 0.13180 0.13108
[ reached getOption("max.print") -- omitted 7000 entries ]y_posterior$beta[,3] #Gen [1] -0.00106944 0.01723637 -0.00707412 -0.01223233 -0.00727736 0.03102150 0.00780335 0.01196708
[9] 0.00060111 -0.00344965 -0.03276547 0.01570662 -0.03691361 0.01670390 0.00211250 -0.04958785
[17] 0.02476726 -0.01359971 0.01159031 0.02364573 0.03294483 0.03331003 -0.00368468 -0.01160258
[25] -0.03407884 -0.02242436 -0.01664567 -0.00280030 -0.03347919 -0.02060326 -0.05208339 -0.01961318
[33] -0.02815123 0.00331478 -0.01284463 -0.01367274 -0.02595851 -0.01838227 -0.00002843 -0.03618458
[41] -0.01911992 0.00769638 -0.02233802 -0.01631122 -0.03514461 -0.03382256 -0.04279913 -0.05639932
[49] -0.00841101 0.02908361 -0.01428011 -0.03452012 0.00285543 -0.04276985 0.02530807 0.02828814
[57] -0.01073406 -0.02072174 -0.01463113 -0.03408588 0.06491618 -0.04641248 0.00018524 0.01746849
[65] 0.02410551 -0.02554732 -0.03213040 -0.03202411 -0.03309063 -0.04789999 0.01868149 -0.01800614
[73] -0.07243857 -0.00036751 -0.00078828 0.01255899 0.01610201 0.02360909 -0.03459102 0.00979454
[81] -0.03137738 -0.00196057 -0.03465186 -0.03683959 -0.00795662 -0.03537149 -0.01463421 -0.02080247
[89] -0.02633286 0.01936775 -0.01110323 -0.03832089 -0.02237906 -0.02946058 -0.04860224 -0.00710820
[97] -0.02651200 -0.01536221 -0.01209059 -0.01820591 -0.02934318 0.02189592 -0.03323197 0.01908551
[105] 0.00734194 -0.00799459 0.00371379 -0.03003360 -0.03243708 -0.00525939 -0.03220836 -0.00280695
[113] 0.01631044 -0.01961994 -0.03633019 -0.02685439 -0.01763903 -0.00592809 -0.00707304 -0.04679316
[121] 0.00601756 -0.01155093 -0.00244633 -0.00423372 0.00627744 -0.00284373 0.01098382 -0.01582925
[129] -0.01773085 0.01162967 0.01780629 0.01568926 -0.01706149 0.01936326 -0.01371813 0.01019764
[137] -0.04970900 0.01175227 -0.00364758 0.00517922 -0.00252040 -0.02988482 -0.03316348 0.00363462
[145] -0.00724080 -0.01502081 -0.00130272 0.00984024 -0.02423766 -0.02989626 -0.05469784 0.01299272
[153] -0.00126771 -0.03893603 0.03084896 0.00541898 0.00169659 -0.02932019 -0.02668506 -0.02836923
[161] -0.01196805 0.01924242 -0.01967250 0.00981299 0.00329113 -0.01166762 -0.03326474 -0.00786504
[169] -0.01477161 -0.00342725 0.00522616 -0.04724767 0.02407782 0.00808069 -0.02637878 -0.03037316
[177] -0.05359858 -0.02430850 -0.02586993 -0.03564347 -0.01689138 -0.01264732 -0.03084462 0.02797533
[185] -0.04126085 -0.01275005 -0.01103491 0.01012007 -0.00796038 0.03335229 0.00607155 0.03412742
[193] 0.01020440 0.00005119 0.03512643 -0.01002376 -0.02842485 -0.04169525 -0.02244722 -0.00388589
[201] 0.00342806 0.00608732 -0.03242061 0.00029119 -0.01230875 0.05841400 0.00844215 -0.01079805
[209] -0.03913770 -0.02528725 0.02791950 0.00358349 -0.00021585 0.02551071 -0.00942989 0.00568125
[217] -0.02119962 0.00631696 0.02434835 0.02028426 -0.05079196 -0.02489043 -0.02409695 -0.02655619
[225] -0.02232372 -0.02316131 -0.02351124 -0.00619559 -0.01888899 -0.03413848 -0.01931235 0.01975430
[233] -0.02900788 -0.04202131 0.02240931 -0.02088645 -0.01760849 -0.05567408 -0.02926210 -0.06655290
[241] -0.02245812 -0.03541059 -0.01808600 0.01946438 0.04481387 0.01558987 0.00997865 -0.00233513
[249] -0.04220607 -0.00127102 -0.01164510 -0.02381192 0.04088900 -0.04206640 -0.01683782 -0.01657179
[257] 0.00162341 -0.03766728 -0.01725767 0.06457781 -0.00709269 0.00489113 -0.00845587 0.00847901
[265] -0.01197224 0.01932045 -0.01149348 0.03788221 0.01766121 -0.04309136 0.00028310 0.00003375
[273] -0.00919706 -0.05949156 -0.00291287 -0.00706582 0.01257272 -0.01914974 0.00177092 -0.00595267
[281] 0.02207284 0.00162421 -0.02896840 -0.06484883 0.03746989 -0.01045558 0.03116725 0.00982722
[289] -0.03014796 -0.00983561 -0.00353908 0.02101640 0.00202344 0.00901520 0.01665022 -0.03458736
[297] -0.02545730 -0.02842022 0.00386381 -0.04129583 -0.03258765 -0.03016905 0.00666764 -0.00822739
[305] 0.02375268 -0.01667201 -0.01582260 -0.02617007 -0.02621151 -0.03164478 -0.02141022 -0.01359232
[313] 0.01896960 -0.02062319 -0.00253318 0.01589958 -0.00132210 -0.00278092 -0.02283437 -0.01221491
[321] -0.04634593 0.01435119 -0.00540659 -0.03972040 -0.00445768 0.00778914 -0.01705931 -0.03095313
[329] -0.00925838 -0.00844864 -0.03830754 -0.03593573 -0.04070685 -0.02078757 -0.00508927 -0.02852232
[337] 0.00658868 -0.02314283 -0.01290900 -0.03449931 -0.03447128 -0.01584038 -0.01826927 -0.00872635
[345] -0.00259939 -0.02339660 0.00486272 -0.05098474 -0.01351135 0.00430683 -0.01654341 0.01790155
[353] 0.00649948 -0.03737773 0.01984770 0.02065052 0.00011394 -0.00545146 0.01981835 -0.02157864
[361] -0.01465082 -0.01822516 -0.05112824 0.00208944 0.03748515 0.00208152 -0.01956229 0.01233414
[369] -0.04405349 0.00366630 -0.01195947 -0.03133940 0.03794244 -0.03524457 -0.01327844 -0.04865635
[377] 0.02815125 -0.00660625 -0.00556524 -0.00046465 -0.02510306 -0.05670267 0.00698370 -0.01587887
[385] -0.01552686 -0.00953153 0.03599736 0.01085109 -0.01905755 -0.00754857 0.00903336 0.02597144
[393] -0.02077984 -0.02312637 0.00081366 -0.00888338 0.01781148 -0.01974338 0.00837198 0.02992075
[401] 0.00016919 -0.01777196 -0.01686568 -0.02472697 0.01683596 -0.00115752 -0.00028660 -0.02589050
[409] -0.03492567 -0.02522668 -0.00248249 -0.03172562 0.00920490 -0.04678724 -0.01257646 0.02196856
[417] 0.00690270 -0.03185368 -0.03081723 -0.02520064 0.00496535 -0.05521860 -0.00479881 -0.06056265
[425] -0.03258747 0.00099649 -0.05228077 -0.02108835 -0.00029120 -0.02813967 -0.04107349 -0.03174505
[433] -0.02113880 -0.01816748 0.03238059 0.00803342 0.03766730 0.01776451 -0.02582345 0.00791820
[441] -0.01096682 0.04238890 -0.00885886 -0.01110628 0.00027975 -0.03184796 0.01112439 -0.01299737
[449] -0.03296547 0.01137316 -0.01462192 0.00325612 -0.01079400 -0.00676842 -0.01052409 0.03477417
[457] 0.00921222 0.01885366 -0.01468075 -0.01808948 -0.02051672 -0.03028779 -0.01178273 -0.02387978
[465] -0.02497720 0.01635957 -0.01762283 -0.05129534 -0.01551434 -0.04395930 -0.00972292 -0.03370353
[473] -0.04263258 -0.05017151 -0.01715730 -0.05990051 -0.04053610 -0.01753843 -0.01544986 0.00373972
[481] -0.05302933 -0.03952766 -0.01989581 -0.01054773 -0.03492909 0.00210697 -0.00374140 -0.02833263
[489] 0.01370584 -0.04438683 -0.03012874 0.00233946 -0.03668764 0.01997901 0.00902279 0.02087751
[497] -0.01892860 -0.01628297 -0.03802780 0.00176971 0.00210700 -0.06014376 -0.02507292 -0.00727020
[505] -0.03372086 0.02196662 -0.00241072 0.00996763 -0.03282617 -0.01049957 -0.00560291 -0.03464997
[513] -0.04086157 -0.01268797 -0.01035984 -0.03935688 -0.01542797 0.04062241 -0.01391082 -0.02681293
[521] 0.00100328 -0.00631745 -0.01628477 -0.00546902 0.03568360 0.02363068 -0.02324745 -0.02622676
[529] -0.01720064 0.01428435 0.01227005 -0.01677244 -0.03239156 -0.00693315 0.00098400 0.00572660
[537] -0.01183543 0.01044649 -0.02488978 0.01333316 -0.03442890 -0.00438871 -0.00700452 -0.02462506
[545] 0.01231491 0.03157708 -0.04024764 -0.00683572 0.00703481 -0.03982794 0.00301000 0.00773979
[553] -0.00683832 -0.05775127 0.01513957 -0.02035579 -0.01687040 -0.03722332 -0.03409375 -0.03492758
[561] -0.03924802 -0.03758798 -0.00606526 -0.02602327 0.00507645 -0.02023009 -0.01311032 -0.00857505
[569] -0.01567366 -0.03882807 0.01846517 -0.03991775 -0.01024226 -0.02290371 -0.01998916 -0.01877028
[577] -0.03044583 0.04748663 0.03546417 -0.00663156 0.00880826 -0.00973563 -0.01853473 -0.00619357
[585] 0.00461061 0.04822562 -0.02162730 0.01028704 0.00990726 0.00205172 -0.03544957 -0.02711232
[593] -0.05796819 -0.03307294 -0.05878819 -0.00919060 -0.01122971 0.00632304 0.00718582 -0.00067684
[601] -0.02942251 -0.01564453 -0.02172695 -0.02362901 0.00516568 -0.01394422 -0.00242754 -0.02801939
[609] -0.00309194 -0.02384221 -0.00436496 0.00883081 -0.00026676 -0.00315559 -0.00132654 0.01611153
[617] -0.00832194 -0.02211942 -0.03257068 0.05218372 0.00183719 -0.01876673 -0.01588151 -0.00646693
[625] -0.01427250 -0.03213229 0.00890484 -0.02481306 -0.00929065 -0.05987277 0.02428045 0.01711637
[633] 0.00730879 -0.00892640 -0.00738414 -0.02997289 0.00979930 -0.00190055 0.00239534 0.00084795
[641] -0.02193393 0.00099078 -0.01187751 -0.03648457 -0.05557616 0.00076893 -0.02145352 -0.02912481
[649] 0.01433732 -0.00123894 -0.00004007 0.01225740 0.01117872 -0.00811510 -0.01604459 0.07323063
[657] -0.04747832 -0.02410094 -0.02893800 -0.00701050 0.01995386 0.02005784 0.01314236 -0.02637196
[665] -0.01619821 -0.02758845 -0.03565105 -0.02334607 0.00242444 0.00265449 -0.02034734 0.01083825
[673] -0.02258150 -0.00504424 0.03422268 0.01223938 -0.00735531 0.04015122 -0.01997087 -0.02554533
[681] 0.04312141 0.01520341 -0.04792968 0.05938690 0.01557169 0.00688373 -0.01566435 -0.00958298
[689] -0.01041508 -0.00472088 -0.04064655 -0.00105047 0.00832744 0.00081818 0.02160404 -0.02580389
[697] -0.00901546 0.00011406 0.00067579 -0.00326265 -0.01492123 -0.03929750 -0.01969733 0.04071537
[705] -0.01357449 0.01363671 -0.02494684 -0.06536937 -0.00732026 -0.03834445 0.00863856 -0.02242512
[713] -0.01224648 -0.03390430 -0.01496557 -0.02922993 -0.01208147 0.02468910 -0.05863531 -0.01225846
[721] -0.02029693 -0.02045500 -0.02450003 0.02054137 0.01162721 -0.05112858 -0.00229661 0.01457119
[729] -0.05279079 -0.01699768 -0.01982464 0.02441704 0.01849751 -0.04370533 -0.03389876 0.00291913
[737] -0.01709731 -0.04704729 -0.01487041 -0.01589592 -0.01093381 0.00743658 -0.05033107 0.01113807
[745] 0.01199589 -0.00869008 -0.03187038 -0.01490141 0.00431069 -0.00836946 -0.01933009 -0.01299569
[753] 0.01939044 -0.01211408 -0.03175936 0.00104045 -0.06231909 -0.00704012 0.00121711 -0.03546202
[761] -0.00837727 0.02803214 -0.02553158 -0.02298883 -0.03858549 -0.01014826 0.01495790 0.02354989
[769] 0.02211439 -0.02566655 -0.00504674 -0.01101805 -0.00692982 -0.03921325 -0.03940566 -0.00749599
[777] 0.03805929 -0.00620814 -0.00374396 -0.02325237 0.02929413 -0.01745231 0.00640088 -0.01962632
[785] -0.04124297 0.01411610 -0.01072105 -0.04168620 -0.01401384 -0.03861442 -0.03007221 -0.02177622
[793] 0.00216091 -0.01711472 -0.00183582 -0.01231198 -0.00530485 -0.02824534 -0.03609937 0.01446428
[801] -0.03790871 -0.03364501 -0.00489917 0.00168372 -0.03390708 -0.01933169 -0.03085077 -0.02672656
[809] -0.02074484 -0.00710802 -0.00882460 0.01411932 -0.08736411 -0.03426144 -0.00185984 0.01118655
[817] 0.00415453 -0.00773135 -0.09541381 -0.00557229 0.00164094 0.00695491 -0.00686187 0.00213255
[825] -0.02446441 0.00598593 -0.00113848 -0.03604497 -0.00185691 -0.01839547 -0.01518696 -0.01472526
[833] -0.01685308 -0.01192389 -0.02214717 -0.04914391 -0.04079834 0.03539863 -0.00171740 -0.03380130
[841] -0.02404942 0.01434104 -0.06482689 -0.07417141 -0.02516215 -0.00641454 -0.05277775 0.01234494
[849] -0.00884694 -0.00240308 0.01558770 -0.00946331 -0.01591890 -0.00097933 -0.01222791 0.00500122
[857] -0.03739292 -0.04379169 0.00310875 -0.00944391 -0.00369027 -0.03458154 -0.03487056 -0.00130277
[865] 0.03485286 -0.01882366 -0.02385289 -0.01841777 -0.03952611 -0.01008472 -0.03851531 -0.00089775
[873] -0.02232514 -0.00249789 -0.03292799 -0.00740002 -0.00287756 0.01614691 -0.01264950 -0.00181115
[881] -0.00624689 0.00931476 -0.02494153 -0.01776656 -0.05625175 -0.02222642 -0.01655510 -0.00704593
[889] 0.01894296 -0.00211182 -0.03411154 0.01283986 -0.04616431 0.00447970 -0.04503459 -0.01860260
[897] -0.02639233 -0.00608991 0.02223530 -0.03133591 -0.03233147 -0.00416417 -0.00527246 -0.00423827
[905] 0.04844717 -0.03769122 -0.00652885 -0.00360693 0.00549933 -0.01422108 0.01877131 0.00452042
[913] -0.03397264 -0.01017858 -0.02275584 -0.01395020 -0.00722969 -0.00690616 0.01087840 -0.02097252
[921] -0.00807237 -0.00529911 0.02903791 -0.02359836 0.00430918 0.01954801 0.03255103 0.00481096
[929] 0.01222095 -0.01531878 -0.07589472 -0.00283592 -0.00971636 -0.02628842 -0.02630826 -0.05259162
[937] 0.02363571 0.02688333 0.01848530 -0.00604214 -0.02716867 -0.02722847 -0.00685417 0.00519733
[945] -0.00065930 -0.02074074 -0.05620331 -0.04628652 0.01696365 -0.01780259 -0.02860517 -0.04008761
[953] -0.02754535 -0.01634527 -0.03466017 -0.01698841 -0.03368713 0.00137538 -0.00001726 -0.02206625
[961] -0.02937552 -0.04308455 0.01940172 0.00874241 -0.02977777 0.00698559 -0.00778609 -0.03506433
[969] -0.02745823 -0.04273358 -0.05083597 0.00930574 -0.00968700 -0.00903485 -0.00566552 0.00208223
[977] -0.00790780 -0.00700300 0.04614389 0.01499214 -0.00985071 -0.00940658 0.01978817 0.00790782
[985] -0.02914472 -0.03217463 0.00200599 -0.01098514 -0.01056695 -0.03509595 -0.02089871 0.00950951
[993] -0.04506481 -0.01342738 -0.02697804 0.01470590 -0.01251625 -0.02685997 -0.01096756 -0.01915778
[ reached getOption("max.print") -- omitted 7000 entries ]y_posterior$beta[,4] #Synt [1] -0.0580709 -0.1255246 -0.0531943 0.0211091 -0.0245510 -0.0957503 -0.0366260 -0.0710829 -0.1038699
[10] -0.1188227 -0.1007875 -0.0316187 -0.0501136 0.0566174 -0.0633323 -0.0683469 -0.0332834 -0.0753876
[19] 0.0208850 -0.0601255 -0.1326091 0.0113301 -0.0607656 -0.0758129 0.0405229 -0.0387246 -0.0875871
[28] -0.0138514 -0.0746292 -0.0794400 -0.0170989 -0.0328154 -0.0426362 -0.0259568 -0.0239526 -0.1126320
[37] -0.0636162 -0.0660164 -0.1617342 -0.1290503 -0.1027480 -0.0177612 -0.0337322 -0.0457715 -0.1003000
[46] -0.0518207 -0.0536642 0.0060373 0.0172356 0.0033403 -0.1160989 -0.0872466 -0.0017243 -0.1050734
[55] -0.0278275 -0.0480012 -0.0626322 -0.0910978 -0.0555417 -0.0660440 -0.0012200 -0.0066243 -0.0041009
[64] -0.0228487 0.0026215 -0.0468868 -0.0560579 0.0170095 -0.0285358 0.0096689 -0.0266034 -0.0441120
[73] -0.0300320 -0.0806484 -0.0071939 -0.0654095 -0.0047029 -0.0612530 -0.0502507 -0.0747559 -0.0191391
[82] -0.0324867 -0.0519815 -0.0173815 -0.0483023 -0.0577793 -0.0494004 -0.0273234 -0.0584371 -0.0510110
[91] -0.0589917 -0.0362908 0.0072645 0.0116599 -0.0189496 -0.0586856 -0.0488691 -0.0263996 -0.0541288
[100] -0.0636438 0.0251891 -0.0408183 -0.0339144 -0.0527506 -0.0193188 -0.0576638 -0.0659842 -0.0419608
[109] -0.0619938 -0.0508279 -0.0967825 -0.0191844 -0.0533217 -0.0677140 -0.0242431 -0.0616770 -0.0086491
[118] -0.1190887 -0.0941562 -0.0145234 -0.0840084 -0.1481985 -0.0433087 -0.0789382 -0.1013058 -0.0217750
[127] -0.0016619 -0.1587711 -0.1009086 -0.1343771 -0.0677475 -0.0759733 -0.1116960 0.0641689 -0.1010369
[136] -0.0623694 -0.1315117 -0.1002803 -0.0180661 -0.0216752 -0.0945420 -0.1175468 -0.0495235 -0.0533487
[145] -0.0949900 -0.0633932 -0.0317941 -0.0084627 0.0310388 -0.0767864 -0.0680118 -0.1259753 -0.0580258
[154] -0.0473656 -0.0623826 -0.0334204 -0.0648701 -0.0045107 -0.0505180 -0.0215068 -0.0378109 -0.0577860
[163] -0.0263118 -0.0705493 -0.0565250 -0.0120375 -0.0847457 -0.0516550 -0.0355564 -0.0800831 -0.0192013
[172] -0.0913282 -0.0019237 -0.0618620 -0.0763390 0.0224207 0.0373938 -0.0861595 -0.0546355 -0.0501233
[181] -0.0650828 0.0653112 -0.0141210 -0.0884818 0.0432456 0.0112641 -0.0843061 -0.1059385 0.0035096
[190] -0.1328496 -0.0076145 -0.0617798 -0.0695282 -0.0505623 -0.1388247 -0.1030055 -0.1089848 -0.0697045
[199] -0.0850279 -0.0159048 -0.0294539 -0.0762410 -0.0555707 -0.0258958 -0.0671594 -0.0501643 -0.0800857
[208] -0.0516575 0.0857000 -0.1400521 -0.0484127 -0.0762083 -0.0401478 -0.0008569 0.0131564 -0.0810089
[217] -0.0994654 -0.0874506 -0.0201230 -0.0539670 -0.0815648 -0.1464876 -0.1169796 -0.0950423 -0.0611003
[226] -0.0741535 0.0816886 -0.0980940 0.0192576 -0.0251506 -0.0894290 0.0014734 0.0019016 -0.0885483
[235] -0.0079345 -0.0116365 -0.0422948 -0.0937649 -0.1224406 0.0339376 0.0549601 -0.0920948 0.0001058
[244] -0.0414216 -0.0889287 -0.0829606 -0.0494142 -0.0312794 -0.0315852 -0.0991131 -0.1456220 0.0435692
[253] -0.0759001 -0.0812450 -0.0643495 -0.0622179 -0.0614507 -0.0329436 -0.0117995 0.0558435 -0.0158497
[262] -0.0913417 -0.0557749 -0.1111105 -0.0822615 -0.0159514 0.0029830 -0.0635864 -0.0359293 -0.0515109
[271] -0.0045787 -0.0450994 -0.0616897 -0.0134001 -0.0415259 -0.0963586 -0.0584049 -0.0729831 0.0546334
[280] -0.0345345 -0.0171604 -0.0542030 -0.0355484 -0.0987536 -0.0621494 -0.0270429 -0.0439867 -0.0855463
[289] -0.0077347 -0.0548824 -0.0689926 -0.0244525 -0.0495526 -0.0629574 -0.0520421 -0.0500731 0.0274207
[298] 0.0013628 -0.0103799 -0.0911320 -0.0469494 -0.0796259 -0.0732215 -0.0456949 -0.0784638 -0.1081318
[307] -0.0290121 -0.1129437 -0.0740280 -0.0607811 -0.0585021 0.0420056 -0.0445469 -0.0316670 -0.0435510
[316] -0.0286988 0.0064144 -0.0554086 -0.0260772 -0.0695439 -0.0337291 -0.0426746 -0.0459226 -0.0237162
[325] -0.0643804 -0.0340856 -0.0623693 -0.0804818 -0.0266953 -0.0875567 -0.0269529 -0.0874816 -0.0704570
[334] -0.0601661 -0.0657173 -0.0293735 -0.0516986 -0.0516427 -0.0325253 -0.1211751 -0.1092165 -0.0843056
[343] -0.1339374 -0.0667145 -0.0438018 -0.0288992 -0.1120275 -0.0407848 -0.0485029 -0.0304792 -0.0242671
[352] -0.1029410 -0.0309156 -0.1135623 -0.0634958 -0.0457185 0.0160404 0.0107945 -0.0239092 -0.0298240
[361] -0.0989963 0.0211364 -0.0823153 -0.0603631 -0.1170338 -0.0884012 -0.0651281 0.0137594 -0.0370961
[370] -0.0854204 -0.0690124 -0.0674919 -0.0152692 -0.0472213 -0.0423105 -0.0409133 -0.0037623 -0.0238612
[379] -0.0951537 -0.0939940 -0.0640982 -0.0569727 -0.0351607 -0.0639275 -0.1161663 -0.0586784 -0.1101348
[388] -0.0553361 -0.0865598 -0.1067731 0.0416231 -0.0664696 -0.0569529 -0.0165446 -0.0369655 0.0119604
[397] -0.0940155 -0.0266589 -0.0041998 -0.0525600 -0.0315629 -0.0250471 -0.0145197 -0.1054418 0.0100043
[406] 0.0010223 -0.0645040 -0.0854113 0.0294252 -0.0162946 -0.0192479 -0.0570641 -0.1231370 -0.0484145
[415] -0.0705734 -0.0670685 -0.0880902 -0.0351656 -0.0377941 -0.0564721 0.0206583 -0.1091387 -0.0366527
[424] -0.0672227 -0.1053186 -0.0750971 -0.0729764 -0.0547614 -0.0176312 -0.0480856 0.0134970 -0.0603522
[433] -0.1110494 -0.0815636 -0.0460410 0.0057996 -0.1269270 -0.0375494 -0.0127186 -0.0413153 -0.0925477
[442] 0.0056823 -0.0100834 -0.0185128 -0.0514942 -0.0949287 -0.0371059 0.0137647 0.0238620 -0.0148203
[451] -0.0147607 -0.0327465 -0.1213411 -0.0884793 -0.0054553 -0.0484142 -0.0928860 -0.0740342 -0.0707859
[460] -0.0962019 -0.0358688 -0.0349636 -0.0499158 -0.0167797 -0.0396238 -0.0461220 -0.0597468 -0.0330944
[469] -0.0663076 -0.0990223 -0.1066612 -0.0292156 -0.0742786 0.0023520 -0.0587219 -0.0415469 -0.0618586
[478] -0.0142731 -0.0314723 0.0141744 -0.0375430 0.0236752 -0.0876268 -0.0686316 -0.0097024 -0.0625698
[487] -0.0649667 0.0005447 -0.0732771 -0.1139303 -0.1406167 -0.0135691 -0.0128949 -0.0321518 -0.0325741
[496] -0.0682385 -0.0365471 -0.1306812 -0.0579546 -0.0492860 0.0278489 -0.0114363 -0.0536266 -0.0449961
[505] -0.0178958 -0.0619981 0.0017643 -0.0686702 -0.0402161 -0.0635116 -0.0086282 -0.0491020 -0.0287860
[514] -0.0009778 -0.0717235 -0.0101734 0.0311278 -0.0465415 -0.0080350 -0.0375403 -0.0984735 -0.0316285
[523] -0.0643713 -0.1292523 -0.1463736 -0.0631842 -0.0494830 -0.0902800 -0.1149235 -0.0895128 -0.0297794
[532] -0.0765637 0.0372551 -0.0306406 -0.0828848 -0.0136323 -0.0286491 -0.1016975 -0.0580203 -0.0027378
[541] -0.0989857 -0.0418428 -0.0890605 -0.1246836 -0.0289774 0.0091468 -0.0028894 -0.0685231 -0.0757349
[550] -0.0423850 -0.0342308 -0.1517858 -0.0391310 0.0083874 -0.0776377 -0.0381384 -0.0921305 -0.0753580
[559] -0.0853246 -0.0649890 -0.0433499 -0.0283672 -0.0861330 -0.0787500 -0.0797864 -0.0419311 -0.0873742
[568] -0.0595298 -0.0904336 -0.0338434 -0.1120734 -0.0941751 -0.0471597 -0.0347115 -0.0551496 0.0166354
[577] -0.0444797 -0.0767432 -0.0460097 -0.0047036 -0.0377310 -0.0888965 -0.0056026 -0.0838246 0.0071702
[586] -0.0915864 -0.0191924 0.0118730 -0.1080674 0.0145589 0.0001870 -0.0416570 -0.0264733 -0.0479717
[595] -0.0483762 -0.1190131 -0.0117415 -0.0214343 0.0022974 -0.0759233 -0.0629609 -0.0555673 0.0058426
[604] -0.0936200 -0.0077048 -0.0308749 -0.0507123 0.0521713 -0.0583258 -0.0118998 -0.0404513 -0.0962759
[613] -0.0629154 -0.0771383 -0.0667545 -0.0943365 -0.0284222 -0.0934684 -0.0467909 -0.0964143 -0.0752365
[622] 0.0028092 -0.0308024 -0.0777274 -0.0615282 -0.0728961 -0.0078966 -0.0728099 -0.0428642 -0.1239330
[631] -0.0306936 -0.0852184 -0.0968696 0.0463589 -0.0556192 -0.0650360 -0.0753129 -0.0225595 -0.0491702
[640] 0.0076265 -0.0575809 -0.0269407 -0.0854585 -0.0548602 0.0071432 -0.0680956 -0.0810258 -0.0262273
[649] -0.0179745 -0.0924814 0.0092550 -0.0882873 -0.1045615 -0.0586414 -0.0372997 -0.0372705 -0.0891078
[658] -0.0335533 -0.0757536 -0.0183747 0.0126745 -0.0574951 -0.0078025 -0.0418091 0.0039441 -0.0175751
[667] -0.0499059 0.0133153 -0.0265012 -0.0600198 -0.0675733 -0.0824336 -0.0523336 -0.0530426 -0.0221714
[676] -0.1632830 -0.0617365 -0.0866244 -0.0554014 -0.0530485 0.0018667 -0.0581943 -0.0529550 0.0198446
[685] -0.0825239 -0.0410522 0.0275531 -0.0537602 -0.1160113 -0.0678019 -0.0228692 -0.0595094 -0.0199657
[694] -0.0319110 -0.0295339 -0.1306761 -0.0030895 -0.0408108 -0.0564524 -0.0594559 -0.0170114 -0.0582647
[703] -0.0910905 -0.1379280 -0.0118500 -0.1256541 0.0137677 -0.0317303 -0.0487279 -0.0260480 -0.0034782
[712] -0.0243240 -0.0935811 -0.0138758 -0.0525151 -0.0512904 -0.1513964 -0.0825594 -0.0383921 -0.0849249
[721] -0.0812503 -0.0809393 -0.0357952 -0.0639331 -0.0524798 -0.0215323 0.0074074 -0.0426281 -0.1129249
[730] -0.0683460 -0.0330312 -0.0469217 -0.0950688 -0.1139316 -0.0834383 -0.1378749 -0.0213169 -0.1347814
[739] -0.0772514 -0.0228314 -0.0360555 -0.0797061 -0.0251391 -0.0315608 -0.1080079 -0.1331927 -0.0721431
[748] 0.0120728 -0.0580965 -0.0108570 -0.0542894 -0.0558066 0.0271923 -0.0840641 -0.0635376 -0.0471780
[757] 0.0030720 0.0107691 -0.0649546 -0.0167415 -0.0375110 -0.0382323 -0.0863283 -0.0425962 -0.0473345
[766] -0.0538690 -0.0423341 -0.0646675 -0.0137288 -0.0082135 -0.0765607 -0.0802003 -0.0485499 -0.0777823
[775] -0.1231128 -0.0483810 -0.0640275 -0.0692970 -0.0142725 -0.0540057 0.0254805 -0.0628860 -0.1037064
[784] -0.0331070 -0.0202918 -0.0898621 -0.0635626 -0.0144646 -0.0639838 -0.0012904 -0.0179106 -0.0208269
[793] -0.0426377 -0.0146822 -0.0104251 -0.0336804 -0.0833945 -0.0997342 -0.1032926 -0.0899683 -0.1100991
[802] -0.0190055 -0.0492793 -0.0420937 0.0052866 -0.0805461 -0.0617757 -0.0258974 -0.0192520 -0.0423604
[811] 0.0410904 -0.0436443 -0.1056956 -0.0469403 -0.1172835 -0.0189209 -0.0151243 -0.0239779 -0.1144203
[820] -0.0451806 -0.0488542 -0.0515097 -0.0721758 -0.0559085 -0.0695870 -0.0910261 -0.0806343 -0.1650156
[829] -0.0366731 -0.0422207 -0.0147527 -0.0911082 -0.0503074 -0.1035620 -0.0524144 -0.0984727 -0.0036500
[838] -0.0076330 -0.1016605 -0.0513939 -0.0823356 -0.0481003 -0.0990802 -0.0163779 -0.0569816 -0.1179487
[847] -0.0492650 -0.1454959 -0.1040295 -0.1018497 -0.0051481 -0.1107469 -0.0377337 -0.0480553 -0.0152915
[856] -0.0004498 -0.1055547 -0.0461308 -0.0001680 -0.0476518 -0.1362110 -0.0280942 -0.0418220 -0.1076138
[865] -0.0064124 0.0363456 -0.1036619 -0.0171969 -0.0835054 -0.0893393 -0.0567807 -0.0604021 -0.1331360
[874] 0.0099883 -0.0682999 -0.0310496 -0.0135102 -0.0746947 -0.0650920 -0.0830484 -0.0338526 -0.0573135
[883] -0.0102278 -0.0349917 -0.0510138 -0.0163819 -0.0736709 -0.0946622 -0.1052607 -0.1272375 -0.0022206
[892] -0.0291186 -0.0818223 -0.0257542 -0.0053992 -0.0646445 -0.0170949 0.0060151 -0.0395360 -0.0805634
[901] -0.0748053 -0.0618500 -0.0535615 -0.0536990 -0.0398397 -0.0049859 -0.0360675 -0.0533977 -0.1031987
[910] -0.0394403 -0.0434704 -0.0573012 -0.0514706 -0.0866545 -0.0231664 -0.0150252 -0.0396506 -0.0364783
[919] -0.0480184 -0.1260701 -0.1235779 -0.0573749 -0.0297702 -0.0568597 -0.0452084 -0.0532208 -0.0280845
[928] -0.0802876 -0.0633139 -0.0080881 -0.0445927 -0.0537926 -0.0233476 -0.0409836 -0.0238367 -0.0459161
[937] -0.0742592 -0.0587913 -0.0198683 -0.0343025 -0.0732501 -0.0268379 0.0051605 -0.0485064 -0.0556865
[946] -0.0601430 -0.0384196 -0.0653317 -0.0369426 0.0270919 -0.1234232 -0.0168269 -0.0201091 -0.0339656
[955] -0.0782871 -0.0244635 -0.0515288 -0.0110918 -0.0381850 -0.0052963 -0.0744787 -0.0893083 -0.0543988
[964] -0.0380151 -0.0468271 -0.0922156 -0.0428788 -0.0338061 -0.0508924 -0.0672524 0.0097903 -0.0471493
[973] -0.0751957 -0.0475501 -0.0759731 -0.0695680 0.0248479 -0.0006723 -0.1135022 -0.1093690 -0.0366890
[982] -0.0736169 -0.0385838 -0.1455670 -0.0756360 -0.0541503 -0.0207080 -0.0313012 -0.1082914 -0.0817355
[991] -0.0368730 -0.0309640 0.0412238 -0.0693011 -0.0494924 -0.0848390 -0.0290980 -0.0425895 -0.0232912
[1000] -0.0520950
[ reached getOption("max.print") -- omitted 7000 entries ]y_posterior$beta[,5] #Lex [1] -0.069098 -0.116367 -0.124565 -0.176420 -0.093531 -0.116993 -0.144602 -0.122323 -0.156137 -0.085809
[11] -0.112633 -0.055190 -0.011962 -0.140794 -0.139883 -0.083894 -0.122228 -0.095080 -0.143504 -0.095749
[21] -0.088869 -0.074267 -0.100710 -0.078606 -0.154985 -0.140247 -0.154963 -0.139098 -0.126072 -0.111806
[31] -0.078000 -0.112447 -0.092223 -0.150971 -0.107237 -0.066879 -0.131230 -0.019444 -0.063071 -0.045208
[41] -0.057779 -0.104169 -0.172058 -0.122537 -0.102102 -0.078335 -0.136413 -0.163440 -0.165446 -0.075063
[51] -0.040287 -0.116522 -0.152285 -0.086492 -0.193180 -0.156147 -0.135968 -0.154164 -0.112217 -0.111301
[61] -0.131721 -0.141640 -0.095810 -0.149869 -0.117142 -0.060183 -0.142303 -0.179804 -0.130077 -0.183636
[71] -0.138277 -0.125884 -0.137317 -0.046757 -0.148556 -0.139671 -0.175938 -0.124494 -0.117983 -0.077199
[81] -0.165997 -0.175225 -0.114825 -0.131158 -0.104889 -0.094946 -0.016849 -0.130537 -0.105199 -0.158697
[91] -0.075878 -0.138758 -0.127239 -0.081171 -0.204199 -0.099366 -0.135095 -0.113480 -0.127146 -0.130908
[101] -0.126751 -0.140049 -0.067722 -0.082295 -0.094392 -0.094218 -0.149414 -0.124576 -0.116494 -0.126512
[111] -0.149175 -0.102982 -0.088846 -0.127740 -0.084759 -0.112960 -0.056006 -0.094348 -0.065176 -0.147071
[121] -0.162986 -0.088359 -0.091713 -0.129109 -0.120877 -0.151758 -0.123199 -0.137083 -0.072490 -0.058929
[131] -0.126569 -0.065289 -0.088674 -0.152853 -0.097695 -0.098881 -0.056658 -0.110842 -0.096446 -0.156610
[141] -0.045022 -0.044527 -0.120854 -0.135080 -0.056219 -0.120617 -0.117988 -0.156476 -0.127451 -0.095089
[151] -0.139622 -0.048504 -0.147525 -0.152364 -0.068968 -0.123273 -0.067668 -0.170196 -0.139110 -0.143617
[161] -0.040625 -0.076441 -0.134336 -0.102612 -0.122604 -0.144819 -0.138918 0.007022 -0.159193 -0.144185
[171] -0.095042 -0.096850 -0.179706 -0.125446 -0.106241 -0.141298 -0.157819 -0.059944 -0.171304 -0.116033
[181] -0.144794 -0.223248 -0.118792 -0.104402 -0.069710 -0.177247 -0.107289 -0.094903 -0.115140 -0.127881
[191] -0.122409 -0.062454 -0.066658 -0.155928 -0.102893 -0.069690 -0.149533 -0.103472 -0.087585 -0.065465
[201] -0.122360 -0.070461 -0.082132 -0.127566 -0.072200 -0.170826 -0.137336 -0.092408 -0.128416 -0.092785
[211] -0.138566 -0.146117 -0.161052 -0.095728 -0.180499 -0.138197 -0.037619 -0.089301 -0.146439 -0.140211
[221] -0.072404 -0.117175 -0.069755 -0.107917 -0.077235 -0.055443 -0.156196 -0.038115 -0.120134 -0.128018
[231] -0.123712 -0.175368 -0.175735 -0.109667 -0.101682 -0.099033 -0.152713 -0.054651 -0.084967 -0.168308
[241] -0.174038 -0.081800 -0.091868 -0.084585 -0.122413 -0.137865 -0.137064 -0.116848 -0.007885 -0.105092
[251] -0.115108 -0.138097 -0.044377 -0.089559 -0.070295 -0.108877 -0.032250 -0.183496 -0.099876 -0.211644
[261] -0.034692 -0.158677 -0.109249 -0.069533 -0.099716 -0.187962 -0.177633 -0.106010 -0.141056 -0.169463
[271] -0.133946 -0.152625 -0.081062 -0.154919 -0.098120 -0.081864 -0.102095 -0.098583 -0.132146 -0.124000
[281] -0.065467 -0.121637 -0.052192 -0.011481 -0.094977 -0.145679 -0.127279 -0.109214 -0.150877 -0.159692
[291] -0.161525 -0.171950 -0.075468 -0.100357 -0.044047 -0.096893 -0.084282 -0.181009 -0.165792 -0.104579
[301] -0.102356 -0.093110 -0.134265 -0.116732 -0.155410 -0.082044 -0.097050 -0.139107 -0.068881 -0.112775
[311] -0.170252 -0.070816 -0.115779 -0.139770 -0.108304 -0.093576 -0.124749 -0.147805 -0.194439 -0.093760
[321] -0.158358 -0.115964 -0.179090 -0.111356 -0.147398 -0.124595 -0.116036 -0.114652 -0.074998 -0.058483
[331] -0.147456 -0.105224 -0.130534 -0.107753 -0.080887 -0.122855 -0.126424 -0.125247 -0.115554 -0.082291
[341] -0.086678 -0.096080 -0.108873 -0.106874 -0.046461 -0.083879 -0.082836 -0.128702 -0.154181 -0.082319
[351] -0.107059 -0.120464 -0.099665 -0.100041 -0.150055 -0.074635 -0.170327 0.006105 -0.108806 -0.122475
[361] -0.055386 -0.197177 -0.092819 -0.069671 -0.108020 -0.140051 -0.113065 -0.154787 -0.100930 -0.083619
[371] -0.057773 -0.117473 -0.132861 -0.092716 -0.128280 -0.042223 -0.155005 -0.097156 -0.099035 -0.065588
[381] -0.069124 -0.159454 -0.120879 -0.068677 -0.157738 -0.089619 -0.052432 -0.114687 -0.124194 -0.152423
[391] -0.146954 -0.050868 -0.152589 -0.123008 -0.115726 -0.135665 -0.098199 -0.190340 -0.111083 -0.143152
[401] -0.132259 -0.125619 -0.133972 -0.026739 -0.138902 -0.117914 -0.110430 -0.103129 -0.130369 -0.184670
[411] -0.113156 -0.115579 -0.101403 -0.145838 -0.041026 -0.040356 -0.150894 -0.113499 -0.081575 -0.116458
[421] -0.186911 -0.049350 -0.169083 -0.109962 -0.078816 -0.145057 -0.084698 -0.144987 -0.111827 -0.052508
[431] -0.159121 -0.086030 -0.132064 -0.119283 -0.212735 -0.151569 -0.067665 -0.066687 -0.147534 -0.124278
[441] -0.066118 -0.157946 -0.184822 -0.141101 -0.148307 -0.144094 -0.158824 -0.192085 -0.110417 -0.115749
[451] -0.140390 -0.092111 -0.082500 -0.105898 -0.104195 -0.128391 -0.124721 -0.137544 -0.103926 -0.122176
[461] -0.015579 -0.072952 -0.140538 -0.165148 -0.185463 -0.135608 -0.097107 -0.060435 -0.021274 -0.122633
[471] -0.106152 -0.096968 -0.039941 -0.093340 -0.143502 -0.099747 -0.119122 -0.072401 -0.176169 -0.140081
[481] -0.144500 -0.109760 -0.112538 -0.065794 -0.078026 -0.138208 -0.062519 -0.141922 -0.130767 -0.065247
[491] -0.118856 -0.143466 -0.182852 -0.128671 -0.175253 -0.172703 -0.093260 -0.108746 -0.086384 -0.178275
[501] -0.145730 -0.049612 -0.152779 -0.117684 -0.183302 -0.132725 -0.131466 -0.081131 -0.118209 -0.094710
[511] -0.209347 -0.149631 -0.161049 -0.141967 -0.085301 -0.127151 -0.144510 -0.143535 -0.184263 -0.174404
[521] -0.096924 -0.079024 -0.113099 -0.026106 -0.147682 -0.129701 -0.119056 -0.073954 -0.105948 -0.188146
[531] -0.096938 -0.092715 -0.145587 -0.105246 -0.089751 -0.155716 -0.169169 -0.076096 -0.107101 -0.204504
[541] -0.019519 -0.141736 -0.101414 -0.089609 -0.134715 -0.170632 -0.124851 -0.101802 -0.132508 -0.085012
[551] -0.187648 -0.025306 -0.125212 -0.169976 -0.084018 -0.120062 -0.137456 -0.108751 -0.149719 -0.109713
[561] -0.145995 -0.131225 -0.068013 -0.096919 -0.102522 -0.090263 -0.127192 -0.144362 -0.102319 -0.041916
[571] -0.079321 -0.063572 -0.101071 -0.172347 -0.169643 -0.147326 -0.151813 -0.110167 -0.074019 -0.145063
[581] -0.121036 -0.145256 -0.168465 -0.070203 -0.170692 -0.051954 -0.080308 -0.128755 -0.132380 -0.108236
[591] -0.123430 -0.135819 -0.061696 -0.063155 -0.111409 -0.192680 -0.136550 -0.023158 -0.048584 -0.088090
[601] -0.083681 0.003982 -0.173000 -0.056182 -0.126635 -0.151683 -0.086074 -0.186393 -0.056276 -0.107955
[611] -0.074466 -0.055978 -0.110506 -0.125348 -0.141874 -0.082598 -0.142706 -0.152668 -0.105907 -0.051033
[621] -0.115839 -0.044170 -0.209931 -0.106288 -0.131804 -0.122321 -0.119186 -0.149995 -0.131558 -0.058133
[631] -0.106718 -0.104906 -0.143172 -0.148841 -0.125667 -0.065137 -0.132165 -0.138331 -0.077691 -0.158182
[641] -0.068526 -0.139713 -0.080221 -0.142450 -0.126029 -0.150428 -0.153236 -0.134201 -0.141065 -0.030868
[651] -0.185758 -0.098862 -0.094789 -0.135163 -0.077312 -0.153485 -0.101439 -0.071731 -0.067044 -0.103930
[661] -0.151210 -0.139346 -0.090040 -0.159072 -0.158595 -0.168187 -0.143937 -0.116629 -0.136383 -0.058703
[671] -0.112681 -0.122869 -0.105477 -0.108287 -0.118301 -0.095424 -0.153006 -0.112280 -0.073689 -0.045380
[681] -0.152159 -0.116220 -0.118116 -0.229255 -0.048394 -0.087643 -0.207431 -0.132912 -0.096813 -0.029010
[691] -0.136544 -0.090841 -0.060851 -0.086266 -0.166369 -0.080622 -0.090513 -0.118800 -0.182897 -0.041249
[701] -0.110380 -0.130980 -0.204663 -0.080105 -0.159053 -0.062329 -0.143764 -0.124914 -0.113763 -0.138006
[711] -0.081738 -0.113786 -0.053474 -0.142461 -0.095527 -0.102213 0.000860 -0.065075 -0.052158 -0.128032
[721] -0.058360 -0.075632 -0.097314 -0.070790 -0.124074 -0.089446 -0.098695 -0.105144 -0.025761 -0.126140
[731] -0.032964 -0.105709 -0.046161 -0.091076 -0.065558 -0.087558 -0.107808 -0.030521 -0.046847 -0.081190
[741] -0.123736 -0.111015 -0.153036 -0.125532 -0.165401 -0.061270 -0.079094 -0.163678 -0.201571 -0.113225
[751] -0.058136 -0.146035 -0.100117 -0.146731 -0.130332 -0.147020 -0.154254 -0.170282 -0.104872 -0.141220
[761] -0.124929 -0.143690 -0.109699 -0.114132 -0.097816 -0.142072 -0.086402 -0.115846 -0.103915 -0.094714
[771] -0.091562 -0.079969 -0.188586 -0.048380 -0.093601 -0.061874 -0.176336 -0.087420 -0.150279 -0.095198
[781] -0.151466 -0.072720 -0.094956 -0.097437 -0.212711 -0.121827 -0.142938 -0.113769 -0.077620 -0.163314
[791] -0.136774 -0.019915 -0.148913 -0.112889 -0.119581 -0.089430 -0.096565 -0.066426 -0.091519 -0.117582
[801] -0.067381 -0.160887 -0.046652 -0.132583 -0.153491 -0.131491 -0.111374 -0.126493 -0.050485 -0.089691
[811] -0.128448 -0.071336 -0.087850 -0.171133 -0.148290 -0.120115 -0.071404 -0.117494 -0.072152 -0.130735
[821] -0.136963 -0.110370 -0.130002 -0.088247 -0.116334 -0.094657 -0.039650 -0.026471 -0.148814 -0.127841
[831] -0.140511 -0.129164 -0.067736 -0.042645 -0.164376 -0.051087 -0.103615 -0.139784 -0.053773 -0.097075
[841] -0.149658 -0.011753 -0.120016 -0.099116 -0.107369 -0.159037 -0.197443 -0.105952 -0.073051 -0.117559
[851] -0.131199 -0.103488 -0.142457 -0.068585 -0.107818 -0.094705 -0.041710 -0.124898 -0.086966 -0.090774
[861] -0.019987 -0.146838 -0.175867 -0.061864 -0.169124 -0.202551 -0.098080 -0.027220 -0.102020 -0.125510
[871] -0.138921 -0.163541 -0.110454 -0.176972 -0.144316 -0.069125 -0.146512 -0.036729 -0.055412 -0.028038
[881] -0.127364 -0.134999 -0.075482 -0.124951 -0.104623 -0.157149 -0.145363 -0.130062 -0.070864 -0.074599
[891] -0.151363 -0.123065 -0.086094 -0.106750 -0.117897 -0.130176 -0.138050 -0.111307 -0.159653 -0.034365
[901] -0.051216 -0.135901 -0.059939 -0.126474 -0.136822 -0.131682 -0.130941 -0.117870 -0.090600 -0.137274
[911] -0.178629 -0.018405 -0.131559 -0.146985 -0.101355 -0.061634 -0.109037 -0.044171 -0.115071 -0.077297
[921] -0.076888 -0.094813 -0.107897 -0.076449 -0.130404 -0.045173 -0.120664 -0.075043 -0.012034 -0.142059
[931] -0.114466 -0.031755 -0.155769 -0.103752 -0.046563 -0.078957 -0.072355 -0.082444 -0.133971 -0.142465
[941] -0.074725 -0.126174 -0.146304 -0.147849 -0.080413 -0.118043 -0.118303 -0.093764 -0.175888 -0.138776
[951] -0.075901 -0.138565 -0.107713 -0.080652 -0.099531 -0.102432 -0.108291 -0.099210 -0.107402 -0.090275
[961] -0.079868 -0.125617 -0.144289 -0.130939 0.028052 -0.097770 -0.166373 -0.083253 -0.121260 -0.163023
[971] -0.170044 -0.140098 -0.035504 -0.164303 -0.092125 -0.168608 -0.149066 -0.112349 -0.095561 0.014181
[981] -0.143994 -0.084143 -0.135315 -0.074579 -0.144324 -0.104185 -0.180882 -0.121829 -0.116332 -0.093875
[991] -0.140605 -0.185408 -0.151596 -0.097202 -0.091434 -0.051397 -0.115125 -0.114293 -0.149513 -0.129991
[ reached getOption("max.print") -- omitted 7000 entries ]y_posterior$beta[,6] #Gram_x_Synt [1] -0.012053919 0.014754522 0.007144885 0.003049790 -0.011411235 -0.015348895 -0.009031572
[8] 0.009039362 0.016592231 0.020419034 -0.006198488 -0.007743556 0.007777367 -0.002665639
[15] 0.009880354 0.010274372 -0.027354002 -0.021811222 0.032081467 0.015904213 0.021663822
[22] -0.047943630 0.064699402 -0.003641274 0.016484393 -0.001865889 -0.001151370 -0.015335693
[29] 0.033256514 -0.007083530 0.019777815 -0.018994960 -0.019971223 0.031737706 0.014013200
[36] 0.029146026 0.014526868 0.023840107 0.007806686 0.008982127 0.029482104 0.029805211
[43] 0.000862000 0.047751829 0.004780964 0.000361097 0.009224333 0.015614542 0.002960787
[50] 0.002811210 0.000607831 0.000250492 -0.017020925 0.002097159 0.026120180 0.042016801
[57] 0.028003300 0.074489403 -0.010505122 0.033234996 0.010848753 0.034818311 -0.006707679
[64] -0.005343586 -0.054321958 -0.004069811 -0.046587841 0.030966741 0.006207130 0.013901873
[71] -0.002321440 0.004574734 0.022767526 0.001576154 -0.033016234 0.014800641 0.024906337
[78] 0.031443095 -0.021030220 0.010658900 -0.009478767 0.007670494 -0.020670162 0.067108352
[85] -0.024463112 0.044925778 -0.021333040 0.011518457 0.039572143 -0.008955921 0.014621281
[92] 0.015930458 -0.001467913 -0.010837968 -0.054418834 0.013063912 0.011902387 0.003953942
[99] -0.015386521 -0.009284926 0.037822066 0.052793045 0.020167610 0.021943492 0.037433486
[106] -0.001596520 0.042264640 0.024495051 0.009167192 0.026045424 0.012572089 0.006028263
[113] -0.016993474 0.002136769 0.023893247 0.034608592 0.018427484 -0.006040735 0.012260518
[120] 0.009591331 0.009626748 0.036799164 0.010020204 0.023910852 0.031850980 0.013758085
[127] 0.005467012 -0.020167694 0.008634891 0.023729991 0.046289298 -0.006157384 -0.000566425
[134] 0.032965204 0.031391943 0.051088323 0.019522407 0.050655783 -0.028020993 -0.018016362
[141] 0.005923816 -0.015911805 -0.015151022 0.020693562 0.012625658 0.049636517 0.024671339
[148] -0.010663026 0.010876388 0.006438925 0.023421056 -0.011364867 0.042585596 0.048486604
[155] 0.031199181 -0.006564058 0.019550293 0.017265632 0.048816281 -0.054881252 -0.002269258
[162] 0.005412218 0.029061996 0.006378123 -0.019637594 0.026610881 0.009251373 0.056563325
[169] 0.005212768 0.030157807 -0.005499728 0.002358558 0.008711667 0.001641390 0.021277651
[176] -0.024797339 0.021848739 0.013811629 0.015025104 0.006561826 0.046791937 0.044749528
[183] -0.027967058 0.014661229 0.050920277 0.025678659 -0.024421968 -0.000666629 0.010964989
[190] 0.030398099 0.010293043 -0.005542221 0.000389019 -0.011368802 -0.008085105 0.011678237
[197] -0.005438898 0.006733277 0.021156387 0.036478931 -0.005149817 0.005196897 -0.009360939
[204] 0.008200879 0.008228476 0.007929707 0.028210856 0.025038587 0.013782165 0.046781534
[211] 0.050445304 0.012528888 -0.000753913 -0.035657174 0.005037405 0.050636999 0.029250586
[218] 0.021235383 0.000724994 0.034776776 -0.027406108 0.017014528 0.013169300 0.024859776
[225] 0.034634536 0.011830101 -0.010510100 0.017310855 0.020732427 0.018361981 0.049341755
[232] 0.038117915 0.041547190 0.041586174 0.043954156 0.018320186 0.010922747 -0.005859799
[239] 0.006724215 0.002060250 0.023915412 0.015623998 -0.018653858 0.015604860 -0.016343412
[246] 0.050553638 0.014991292 0.014199034 0.044053953 -0.003562401 0.020257409 0.017183454
[253] 0.008845914 0.045592271 0.006887554 0.003949070 0.061227832 0.006664268 0.003188033
[260] 0.012771630 0.022009213 0.016744541 -0.000238822 0.008166556 -0.013924808 0.006805684
[267] 0.003817505 -0.019192657 0.021892371 0.049597519 -0.001202841 0.021979227 0.004517902
[274] 0.019087386 0.022100085 -0.011092042 0.007058215 0.037117740 0.023837507 0.008817305
[281] -0.013990823 -0.006241586 -0.003890364 -0.034854885 0.042098293 -0.002868521 -0.013580604
[288] 0.040512306 -0.004801704 0.004974189 0.006968378 0.000102830 0.003127398 -0.023633128
[295] 0.033897042 -0.012044675 0.007065881 -0.003874734 -0.000754462 0.015776918 0.048706165
[302] 0.022663981 0.020159756 0.022704555 -0.008801243 -0.023002472 0.004670624 0.023382642
[309] -0.000852052 -0.020165830 0.011195817 0.009162465 -0.001099382 -0.021077353 0.022289281
[316] -0.051627648 0.000157403 0.070222834 -0.006244143 0.019957914 0.030901458 0.003225820
[323] -0.007268060 -0.003484104 0.023939353 0.038905545 -0.005255513 0.042758748 0.000372006
[330] 0.015141637 -0.009956753 0.026870998 0.007675974 0.008967939 0.024424123 0.020832624
[337] 0.026632741 -0.012873898 -0.014803293 0.026098420 0.039664665 -0.003050198 0.017589908
[344] 0.007170390 0.025028954 -0.005563575 0.037158467 0.005980765 0.017965216 -0.012339523
[351] 0.003346277 0.025558752 -0.010826337 0.015820284 -0.003262820 0.032833077 0.001429373
[358] 0.044786861 -0.030918742 -0.002428887 0.004334986 0.006103044 0.005175016 0.030853259
[365] 0.007642248 -0.022361483 0.019205251 0.044242301 -0.026414495 -0.010922628 0.006345946
[372] 0.001152588 0.008882038 0.007843064 -0.002538221 0.014874287 -0.001480009 -0.017798511
[379] 0.014858787 0.030301098 0.037023514 -0.017214374 0.039558185 0.056279300 0.007367182
[386] -0.006101727 -0.005720232 -0.013748114 -0.010756730 0.007551587 0.006368365 -0.004272900
[393] 0.006911902 0.010567079 0.016096658 -0.015405292 -0.005442688 -0.004323465 0.009408704
[400] 0.047856718 0.007483488 0.007998062 0.006371931 -0.011529029 0.004587191 0.044781677
[407] 0.023354210 0.033594966 0.044104965 -0.008467016 -0.005118294 0.033089599 0.015013548
[414] 0.039686870 -0.009661252 0.031851627 0.019284280 0.002436709 -0.009942009 0.025920021
[421] 0.023419027 0.006242764 -0.000823480 -0.020710263 0.016774499 0.013601631 0.069076175
[428] -0.028404506 -0.001806817 0.019743867 -0.000571089 -0.008219648 0.008236665 0.028811928
[435] 0.024859711 0.032736719 0.029943147 0.011351226 0.002291657 0.020581127 0.014001618
[442] 0.014934706 -0.032167873 0.003983736 0.024181380 0.035178239 0.012927366 0.039760399
[449] -0.005629029 0.015405629 0.012123241 -0.018592560 0.002181511 0.015171355 -0.013936037
[456] -0.004944377 0.007431961 0.034116533 0.013772340 -0.006508610 -0.013663091 -0.012728986
[463] 0.044357768 0.054914802 -0.013295729 0.000796607 0.055135899 0.038053120 -0.010750937
[470] -0.003407293 0.048667024 0.039655318 0.028360000 -0.011139553 -0.018388722 0.011541109
[477] 0.007668721 0.007457710 0.025817415 0.019856628 0.003284943 -0.015616648 -0.012828690
[484] 0.062811535 -0.019392346 0.035210502 0.045772524 0.051318771 -0.011890894 -0.020404882
[491] 0.005469225 0.006292418 -0.000006148 0.029995801 0.028303511 -0.014172149 0.018505936
[498] 0.049635036 -0.002715721 -0.023563702 0.000396551 0.057023143 0.037678222 0.004514297
[505] -0.014472306 0.031057672 0.017058218 0.041565510 -0.001717792 0.038301211 0.071781959
[512] 0.032048461 -0.025011325 0.020458530 -0.002143665 0.039278700 0.021704579 -0.008806822
[519] 0.018869992 -0.023280891 0.038901766 0.026530751 0.006381008 0.060991905 0.032556930
[526] -0.031139362 -0.016647832 0.058942433 0.022156816 0.004503064 0.056212538 -0.034383235
[533] 0.014465665 0.019392152 -0.003466331 0.014388073 0.006884957 0.009225298 0.015329753
[540] 0.011903942 0.009005449 0.013453775 -0.013199192 0.036351493 0.045748677 0.026582619
[547] 0.024651536 -0.037889066 0.034040895 0.008216849 -0.029054037 -0.019732689 0.012183848
[554] 0.013898221 0.041247424 0.024024532 0.066441407 0.031490212 -0.005688212 0.003497844
[561] -0.035014175 -0.002662352 0.014257729 0.029548115 0.016740242 0.031869100 0.004848812
[568] -0.004331868 0.004338056 -0.018475191 -0.047282759 0.003644762 0.014951040 0.026167329
[575] 0.043889880 0.024999386 0.002589263 0.021193174 0.016907587 0.034450189 0.018299780
[582] -0.001110952 0.022989705 -0.016146686 0.039457983 0.063214249 0.041375807 0.003579335
[589] -0.006704866 -0.002280280 -0.016583614 0.053267314 -0.020037257 -0.006118687 0.005912616
[596] 0.050662193 0.028214021 0.009168763 0.018450950 -0.021918623 -0.001668109 0.036464723
[603] 0.015674621 0.001316117 -0.015252696 -0.019400787 0.012349184 0.004159622 0.003293789
[610] 0.026441957 0.043647602 0.030932162 -0.016300561 0.035343431 0.031774570 0.013181647
[617] 0.005420667 -0.017318452 0.003509716 0.033422271 0.025982079 0.039929521 0.038821851
[624] -0.018944550 0.016694112 0.006658130 0.015950420 0.009074993 0.005294001 -0.003015660
[631] 0.015526018 -0.006717745 0.003552571 -0.018544149 -0.005691882 0.020992950 -0.012617800
[638] 0.022345279 -0.000039778 -0.010707206 0.021541263 0.042733030 0.011347959 -0.007234615
[645] 0.039906873 -0.013909065 0.006361666 0.020702706 -0.006860667 0.031063582 0.066136145
[652] 0.048047799 0.032720913 0.002822924 0.023648236 0.007544186 0.018143277 0.020396678
[659] 0.016838428 0.000498562 0.034176210 0.013587618 0.035882788 0.028280884 0.017033557
[666] 0.020445896 0.011110946 0.000036181 0.026962838 -0.005930062 0.028811710 0.039446787
[673] 0.029749166 0.001960003 0.022929681 -0.005712091 0.034676024 -0.017238645 0.031562640
[680] 0.003815196 0.015994894 0.011774437 0.018343626 0.023858496 0.019682342 0.015071338
[687] 0.048596945 0.027659239 0.065850736 -0.037221541 0.047056788 0.029936660 0.050982556
[694] 0.023943165 -0.021755629 0.002741206 0.002990121 0.032345890 0.021159455 0.034310716
[701] 0.040598341 -0.008942137 0.023064678 0.035793727 0.039702970 -0.002390951 0.005644896
[708] 0.009629626 -0.033680237 0.030776276 0.001536231 0.001253315 0.004417287 0.017071821
[715] 0.035959268 0.009330723 0.023285293 -0.004454930 -0.020623033 0.026082472 -0.022438432
[722] 0.008329827 0.025014950 0.026363217 -0.012304137 0.049365428 0.019805067 0.052183807
[729] -0.008430330 0.040307198 -0.018826765 0.016490785 0.061984964 0.005365587 0.044444357
[736] 0.018585105 -0.003226290 0.021049175 -0.010551526 0.001663555 0.048180334 0.048653505
[743] 0.015090386 -0.010431401 0.029503216 0.006924384 0.008781463 0.004555504 0.000042913
[750] 0.017933355 0.031648791 -0.004329365 -0.045191145 0.040187157 0.072329904 0.014598781
[757] -0.003412739 0.002453786 0.043264804 0.009777457 -0.007603551 0.027443990 0.042178667
[764] 0.030974086 0.034262699 0.008403341 -0.036547959 0.040874872 0.035146108 0.018878237
[771] -0.043905149 0.038710184 0.028068466 0.022390705 0.029764010 0.057009173 0.045727026
[778] 0.012067397 -0.006341294 0.000468020 0.043068943 -0.005756593 -0.025453610 0.034412938
[785] 0.021323645 -0.001644282 0.004806446 -0.000568906 0.010519139 0.009240913 -0.002436806
[792] -0.021200304 -0.024830389 0.036075209 -0.014899199 -0.012150831 0.002999114 0.002242233
[799] 0.039615899 -0.003795963 -0.034565161 0.029131107 0.028073597 0.015484004 -0.032441500
[806] -0.009185958 -0.030228665 0.002726402 -0.017533839 0.003029689 0.028433474 0.041672016
[813] 0.020215372 -0.003961926 0.029731908 -0.024040580 -0.003882222 0.049569214 0.036364653
[820] 0.034613451 0.006963568 0.026591667 -0.038113123 0.027112555 0.021861104 -0.002479497
[827] 0.021054882 0.023035508 -0.004582576 -0.025667750 0.018828927 0.019623567 0.004891520
[834] 0.019820957 0.049042078 -0.021253791 0.032418990 -0.005225294 0.008422118 0.019205023
[841] 0.028316707 0.027250070 0.003329716 0.002001136 0.028099533 0.034584155 -0.043788834
[848] 0.006555839 0.047865522 0.037586149 -0.043947864 0.017350364 0.012340980 0.035062414
[855] 0.034300392 -0.018176239 0.002475874 0.042239447 -0.018509427 -0.009978129 0.019361764
[862] -0.014064001 0.037991739 0.014362099 -0.005386079 0.018146183 0.012368366 0.006431632
[869] 0.063798936 0.057414183 0.010328949 0.029888270 0.030400181 0.013052428 -0.017734332
[876] 0.006395295 -0.000151400 0.009225683 0.011822913 -0.012261209 0.003756356 0.014860968
[883] -0.029842157 0.004751597 0.018930760 0.058817612 0.019487910 0.006237380 0.002761476
[890] 0.024546540 0.019185811 0.026612414 0.006070896 0.030955961 0.035833762 0.006379187
[897] 0.029716378 -0.006818472 0.009990591 0.005757794 0.025198726 -0.066378357 0.009551091
[904] 0.004527612 0.035425435 -0.019127787 0.042022958 -0.026514777 0.003383995 -0.035409381
[911] 0.029203736 0.015934741 -0.010481638 0.012179697 0.008202882 0.023307838 -0.035591125
[918] 0.029511481 0.056864625 0.008800195 0.014822592 -0.023870149 0.028976823 0.022198034
[925] 0.040705885 -0.047019665 0.033661258 0.013396326 0.028056760 -0.004169823 0.014647351
[932] 0.014213520 -0.015714918 0.046278686 -0.007517957 0.016054498 -0.006509788 0.028289573
[939] -0.044324242 0.024723385 -0.044921375 -0.007520176 -0.009162039 0.014690728 -0.018305795
[946] 0.025137231 -0.001063619 0.008165955 0.023708789 0.000319906 -0.002366817 0.004398441
[953] -0.011777940 -0.002110035 -0.000593120 0.026958964 0.005688029 -0.023181042 -0.004643152
[960] 0.029474770 0.045304698 0.014702044 0.051003017 -0.017537408 0.029679523 -0.005188969
[967] -0.005438543 0.026174842 -0.011049007 -0.007329470 -0.002961007 0.026495510 0.014627785
[974] -0.014271443 0.011850878 0.044880864 -0.017744068 0.010378671 0.009961981 0.028902989
[981] 0.015240378 0.020341042 0.032706815 -0.019006661 -0.007384688 -0.007692411 0.002395278
[988] 0.003319761 0.001336142 -0.033420445 0.002486631 -0.019118270 0.037628086 0.030318822
[995] 0.032768004 0.026228418 -0.007470728 -0.016561743 0.053905805 -0.043760279
[ reached getOption("max.print") -- omitted 7000 entries ]y_posterior$beta[,7] #Gram_x_Lex [1] -0.02643453 -0.01451428 -0.04366503 0.01843834 -0.00076347 0.01209294 -0.01748334 -0.02444153
[9] -0.00861787 -0.00017053 -0.03410152 -0.01743597 -0.02001689 -0.05376000 -0.02712873 -0.06246272
[17] -0.04161960 -0.02835483 -0.05913866 -0.03214733 -0.03096222 -0.02073950 -0.05928627 -0.03070344
[25] -0.04068317 -0.00764850 -0.00514830 -0.02318248 -0.01631486 -0.00948557 -0.03242251 -0.00807094
[33] 0.01616959 -0.02220997 -0.03743642 -0.06746575 -0.01641995 -0.03401093 -0.03212801 -0.00781529
[41] -0.03783019 -0.07492733 -0.01885148 -0.04286303 -0.01136275 -0.05469012 -0.02164856 -0.01797525
[49] -0.01709745 0.01297769 0.00492893 -0.04140623 0.00187186 -0.04469084 -0.05977066 -0.01960863
[57] -0.02182371 -0.02860936 -0.00057314 -0.02651102 -0.03320972 -0.04223014 -0.00491344 -0.01838174
[65] -0.00030431 -0.02783298 0.00552627 -0.02060781 -0.02448125 -0.01608656 -0.03611300 -0.05571256
[73] -0.00834680 -0.01216059 0.02897794 -0.02601901 -0.00534867 -0.05330070 -0.05459600 -0.02066640
[81] 0.00650506 -0.01480814 0.00188709 -0.03217758 -0.01216388 -0.04272111 -0.05887711 -0.00675317
[89] -0.00763020 0.06333080 -0.01137966 -0.01303753 -0.02243008 0.01089048 0.00339523 -0.01322859
[97] -0.02061289 -0.02188776 -0.03326726 -0.00747940 0.00735974 -0.03844854 -0.02681083 -0.00438416
[105] -0.03828524 -0.01361433 -0.02964937 -0.04928472 -0.01449879 -0.03342002 -0.02643976 -0.02073341
[113] -0.00642303 -0.03873478 -0.03927047 -0.02899113 -0.03431393 -0.00122088 -0.01698484 -0.03453561
[121] 0.01178450 -0.02792079 0.00487644 -0.00991496 -0.02067221 -0.02640500 0.03070449 -0.00889728
[129] -0.03004693 -0.00921610 -0.06013195 -0.01617879 0.00019325 -0.06166815 -0.01141566 -0.03581983
[137] -0.04069232 -0.02705157 -0.03256755 -0.02324772 -0.05463163 -0.00037038 0.01894868 -0.01947937
[145] -0.02598582 -0.06407870 -0.02393923 0.00193464 -0.01905001 -0.01622242 -0.00808539 0.00696542
[153] -0.06268436 -0.08193457 -0.04844866 -0.03195551 -0.05555786 -0.01196152 -0.04624227 0.01525784
[161] -0.02704867 -0.02411313 -0.00939314 -0.02052374 -0.01043394 -0.03824097 -0.01875809 -0.07668134
[169] 0.00009467 -0.01897002 -0.01413781 -0.00341205 0.01297243 -0.04184697 0.00166877 -0.03664289
[177] -0.02500279 -0.03159881 -0.03543930 -0.01780031 -0.00779482 -0.02567635 -0.03116231 0.01170112
[185] -0.04849484 -0.06606359 -0.05685330 -0.04721787 -0.06716849 -0.05641866 -0.04178808 -0.04574469
[193] 0.00157066 -0.03630107 -0.00731648 -0.02888703 -0.01615027 0.00050683 0.00577121 -0.02481859
[201] -0.01235304 -0.06007567 -0.02038987 -0.02497667 -0.01118532 -0.02253475 -0.03849516 -0.03194632
[209] -0.03574074 -0.01632911 -0.06890123 0.00820960 -0.01653961 -0.03018833 -0.05682885 -0.01695133
[217] -0.04381806 -0.02098045 -0.03217932 -0.05679859 0.00067117 0.00143422 -0.02893429 -0.02479318
[225] -0.02620596 -0.00840995 -0.00045180 -0.01668423 -0.02240569 -0.04227508 -0.07023026 -0.04341890
[233] 0.00781580 -0.03270959 -0.02349330 -0.00357934 -0.02321941 0.01080959 -0.03787897 -0.00615312
[241] -0.05876903 -0.03614358 -0.00551643 -0.02687662 -0.00128653 -0.03177145 -0.04168294 0.02518072
[249] -0.09282864 -0.02642341 -0.03431145 -0.03934078 0.02870356 -0.05774151 -0.02783546 -0.03569581
[257] -0.07292926 -0.00722331 -0.04090171 -0.01447970 -0.04589897 -0.02387058 -0.03472802 -0.01273755
[265] -0.02319917 0.01342241 -0.04729430 -0.05245226 -0.03079458 -0.06435720 0.02829442 -0.04202410
[273] -0.01359534 -0.01886244 -0.05440801 -0.01384375 -0.02794992 -0.04535853 -0.06550394 -0.02447065
[281] 0.02350221 -0.01145367 -0.06212092 -0.02789046 -0.02609297 -0.01245739 -0.01901068 -0.00791684
[289] -0.02932615 -0.04077011 -0.01799057 -0.04467382 -0.02366046 -0.01558504 -0.04466097 -0.03990633
[297] -0.06722660 -0.00392484 0.00892352 -0.01072640 -0.04850431 -0.02594873 -0.03623498 -0.02226579
[305] -0.01834489 0.03161110 -0.01365192 0.00087102 -0.03940325 0.01287436 -0.03159340 -0.05321306
[313] -0.02273264 -0.03843360 -0.02206083 -0.01899047 -0.02088854 -0.04319470 -0.02249226 -0.04861357
[321] 0.00219132 -0.04203135 -0.03798334 -0.01776994 -0.05160029 -0.05476080 -0.01920159 -0.03703027
[329] -0.02549676 -0.04357840 0.01453175 -0.05182269 -0.00830282 -0.01437660 -0.01006008 -0.01647934
[337] -0.03935809 -0.00697240 -0.01028355 -0.01421691 -0.04752911 0.01034952 -0.00098039 -0.01737055
[345] -0.00807782 -0.00222755 -0.04146218 -0.02481229 -0.04292281 -0.04056561 -0.02537366 -0.04769446
[353] -0.02568001 -0.01215094 -0.02712172 -0.00947555 -0.00800113 -0.05020939 -0.00266159 0.00703621
[361] 0.00850367 -0.02741474 -0.00686226 -0.05661119 -0.02526093 -0.03460484 -0.01700057 -0.05144143
[369] -0.02662308 0.01488023 -0.02894203 -0.01582288 -0.01905348 -0.02322706 -0.02525124 0.00967005
[377] 0.01184858 -0.02068330 0.00312128 -0.00356497 -0.06371916 0.00361304 -0.03003813 -0.06700369
[385] -0.02484764 0.00590362 0.00349995 -0.01083284 -0.01657195 -0.00540002 -0.02771871 -0.03236171
[393] -0.03580009 -0.02339241 -0.00128578 -0.01630400 0.02760939 -0.01304896 -0.01068724 -0.04539129
[401] -0.04127351 -0.03084810 0.00704917 -0.05166535 0.01752555 -0.03369183 -0.04827954 -0.05456975
[409] -0.01116630 -0.04834029 -0.03261413 -0.06028317 -0.03889594 -0.02451126 0.02053563 -0.03972262
[417] -0.04829351 -0.01445773 0.01363454 -0.01482907 -0.03484187 -0.03135330 -0.02342801 -0.00874967
[425] -0.03670503 -0.04178076 -0.04424004 -0.00095267 -0.02989046 -0.02578494 -0.00824375 -0.01316815
[433] -0.05947288 -0.03818290 -0.02474425 0.01076816 -0.03226907 -0.06546323 -0.03620571 -0.02867191
[441] 0.00652991 -0.03456909 0.00060652 -0.00450392 0.00692765 -0.01575644 -0.03356154 -0.03562993
[449] 0.02181551 -0.03599489 -0.00669726 0.02768550 -0.01518058 -0.04376911 -0.03730073 -0.01518469
[457] -0.02897032 -0.02771215 -0.02273453 -0.00221906 -0.03668305 -0.01800505 -0.06041597 -0.06079458
[465] -0.03540480 -0.00816749 -0.05353366 -0.00955651 0.02004832 -0.00539581 -0.04658702 -0.07028570
[473] -0.04270199 -0.02865353 0.04171762 -0.01110867 -0.01461266 -0.04050041 0.01035467 0.00510461
[481] -0.00284029 -0.02296470 -0.01694183 -0.04891627 0.01525564 -0.04652637 -0.05354268 -0.03472809
[489] -0.00682341 -0.02765865 -0.00971251 -0.07216920 -0.00047392 0.00040692 -0.01326636 -0.02496738
[497] 0.00966524 -0.02520933 -0.01923726 -0.02539457 0.01922423 0.00417531 -0.00940627 -0.02461765
[505] -0.00314250 -0.01853697 0.00307019 -0.04976035 -0.00967822 -0.05818942 -0.07288053 -0.06726407
[513] -0.03345950 -0.01911275 -0.00734147 -0.02455671 -0.05006015 -0.03188424 -0.04958020 0.02029221
[521] -0.04139600 -0.03849835 -0.02962409 -0.08544189 -0.05858402 -0.00507427 0.01253951 -0.03943135
[529] -0.02393139 -0.03988454 -0.03128781 0.02543767 -0.00332797 -0.02998702 -0.00369736 -0.01095120
[537] -0.03690548 -0.03988520 -0.01907614 -0.03486920 -0.00848036 -0.02674762 0.00604316 -0.01899584
[545] -0.04451674 -0.04579106 -0.05260233 0.00208135 -0.00642935 -0.02685562 -0.01388928 -0.02246385
[553] -0.03530743 -0.05305907 -0.01627333 -0.02262527 -0.06536191 -0.01303825 0.02371155 -0.01437739
[561] 0.01539837 -0.00257150 -0.02118073 -0.05470416 -0.02971560 -0.04779792 -0.01910332 -0.03562463
[569] 0.01901840 -0.07113551 -0.00045447 -0.03477393 -0.04007794 -0.01873713 -0.00935164 -0.01615365
[577] -0.01949977 -0.02209669 -0.02505321 -0.00021634 -0.02646467 -0.01916797 -0.01256380 -0.03622366
[585] -0.00998133 -0.00967243 -0.05353197 -0.00106051 0.02830872 0.00155172 -0.01486582 -0.05610591
[593] 0.00478220 -0.03555491 -0.00020586 -0.06580379 -0.00736039 -0.01997332 -0.04204674 -0.04269298
[601] -0.00095659 -0.03812233 -0.03950344 -0.01183667 -0.02464647 0.02196897 -0.02767469 -0.05377603
[609] -0.02622700 -0.02589430 -0.04512203 -0.01764576 -0.00881160 -0.03662602 -0.04531826 -0.05111312
[617] 0.00631146 0.00300572 -0.02185265 -0.02993809 -0.04926822 -0.03895486 -0.03158369 0.00484968
[625] -0.04499772 -0.02701169 -0.01764699 -0.04690195 -0.01877269 0.00842860 -0.03228362 -0.03044106
[633] -0.02351278 0.00385739 -0.00772301 -0.02563380 -0.01571325 -0.05910754 -0.01366370 -0.01916929
[641] -0.02695644 -0.01627780 -0.03852938 -0.00745844 -0.04802423 -0.00011451 -0.02839234 -0.00081342
[649] -0.07057136 -0.02281155 -0.04421789 -0.03817229 -0.00913516 0.00622887 -0.05324860 -0.01447360
[657] -0.01896764 -0.02803868 -0.03020571 -0.00626744 -0.00638130 -0.05052821 -0.02530809 -0.05480415
[665] -0.03397229 -0.04305835 -0.03251531 -0.00604614 -0.00763012 -0.01283697 -0.05616155 -0.04416624
[673] -0.02759586 -0.04491070 -0.04162526 -0.02827539 -0.04257298 -0.01517922 -0.02056468 -0.00755024
[681] -0.04257063 -0.00056852 -0.01432991 -0.02506194 0.01542658 -0.06204788 -0.07236074 -0.02578789
[689] -0.06081175 0.00458579 -0.04242465 -0.02385211 -0.05580995 -0.02647968 -0.01393687 -0.03061703
[697] 0.01211544 -0.05127699 -0.02595431 -0.02536342 -0.02325093 -0.00654146 -0.01388877 -0.05333031
[705] -0.04726698 0.00693884 -0.00918853 -0.02739936 -0.01325029 -0.04682944 -0.00957618 -0.03964492
[713] -0.02111677 -0.08008734 -0.04465775 0.00690704 -0.03401665 -0.00908369 -0.00863984 -0.04508560
[721] -0.00978252 -0.01768274 -0.05541814 -0.02901092 0.03097187 -0.05214321 0.00824164 -0.05784582
[729] -0.02645794 -0.01321716 -0.01971365 -0.03832905 -0.04708200 -0.04701990 -0.05158652 -0.02120985
[737] -0.01479490 -0.02843450 -0.02928552 -0.03602659 -0.03268052 -0.05175128 -0.02384001 -0.01491974
[745] -0.02707868 -0.05375941 -0.01425614 -0.02529922 -0.00952068 -0.03097688 -0.01268397 0.00693706
[753] -0.01845683 -0.06163163 -0.07336366 -0.00116291 -0.00130428 -0.03219313 -0.05806433 -0.03281992
[761] 0.01173381 -0.04083586 -0.03575711 -0.04609556 -0.04363068 -0.06080074 -0.00624828 -0.05538195
[769] -0.04386118 -0.04864834 0.06944225 -0.00971917 -0.02259751 -0.02683002 -0.06396183 -0.05945182
[777] -0.04036633 -0.03509833 0.00117676 0.02096622 -0.04094549 -0.01094518 0.00407361 -0.06569276
[785] -0.01375686 -0.01501731 -0.02862459 -0.03735221 -0.01913476 -0.02281421 -0.02460915 -0.03664815
[793] -0.01295285 -0.04024895 -0.05770790 -0.03192626 -0.01738563 -0.00395764 -0.01674623 -0.03427450
[801] -0.02147518 -0.05824836 -0.00375304 -0.02417545 -0.00609911 -0.00340914 0.00416113 -0.01724947
[809] -0.01296240 -0.01114690 -0.01393601 -0.03150531 -0.07093549 -0.03727286 -0.01374972 -0.02140981
[817] -0.01117766 -0.06389543 -0.04256110 -0.01610282 -0.05388490 -0.05037226 0.00373292 -0.03173082
[825] -0.08562668 0.04021089 -0.02799692 -0.02697669 -0.02296435 -0.01205416 -0.01801702 -0.04131215
[833] -0.03191431 -0.02749930 -0.03094314 0.02511232 -0.04878524 0.00203434 -0.02310371 -0.05526603
[841] -0.04098999 -0.01760341 -0.03250831 0.01448453 -0.02583955 -0.06986302 -0.01250631 0.01021258
[849] -0.00541269 -0.01768586 0.02768226 -0.05891068 -0.03225762 -0.03637505 -0.06597700 -0.02366252
[857] -0.04140228 -0.01809750 -0.05058498 -0.01955154 0.00526073 0.00648315 -0.01693454 0.00147817
[865] -0.01773661 -0.06452452 -0.05674149 -0.02629286 -0.05809171 -0.02268760 -0.04812062 -0.04948628
[873] -0.01349378 -0.01541379 -0.02916535 -0.03984560 0.00337029 -0.01147897 -0.03951471 -0.02150401
[881] 0.00049595 -0.02769921 -0.04851395 -0.02324474 -0.03763487 -0.05986193 -0.06646502 -0.00968342
[889] -0.02823357 -0.00290011 -0.00151242 -0.01045226 -0.04004993 -0.03712755 -0.06166138 -0.03269784
[897] -0.05236049 0.01094839 -0.01058729 -0.02307828 -0.03213981 0.00144466 -0.04182505 -0.00654216
[905] -0.04707247 -0.01519781 -0.03501768 0.03061429 -0.01760438 -0.01716675 -0.00923068 0.00382860
[913] 0.01132242 0.01623165 -0.01240650 -0.01025050 -0.01051720 -0.03890299 -0.03175194 -0.01688440
[921] -0.02345485 -0.02446062 -0.03428995 -0.06780624 -0.03653889 -0.01374693 -0.03138218 -0.03982395
[929] -0.00770516 -0.01537408 -0.02318678 -0.02471987 -0.00013384 -0.06435879 -0.02400762 -0.06704189
[937] -0.00987989 -0.03854024 -0.00511353 -0.04120990 -0.02916683 -0.01186123 -0.00029060 -0.00581717
[945] -0.00750516 -0.04196529 -0.03758306 -0.00243677 -0.00811729 -0.03648469 -0.00034186 -0.00590233
[953] 0.01633747 0.02212211 -0.02709491 -0.05319853 -0.04668680 -0.01104105 -0.00191937 -0.04990468
[961] -0.05102453 -0.02932010 -0.04953287 0.00019400 -0.03136559 -0.02214163 -0.01452695 -0.05092710
[969] -0.03485967 -0.00402785 -0.01356430 -0.03125674 -0.05122640 0.03440960 -0.03092051 -0.00782390
[977] -0.02169741 -0.01133890 -0.06916662 -0.03715976 -0.01982440 -0.01790209 -0.05805647 -0.05247393
[985] 0.02229157 0.00520565 -0.01108463 -0.02816843 -0.00829501 0.02204537 0.00010049 -0.01426876
[993] -0.05139977 -0.01943905 -0.03282174 -0.03749179 -0.03978463 0.01728317 -0.07666557 0.07984352
[ reached getOption("max.print") -- omitted 7000 entries ]# check predicts --> posterior parameter distr. back in normal space
pst_gram <- y_posterior$Gram
pst_gram [1] 27.10 45.49 43.30 40.76 42.06 41.16 40.56 46.59 48.83 42.73 49.29 42.67 42.40 36.39 46.27 31.79
[17] 33.26 45.11 39.66 44.95 43.86 40.94 30.44 54.60 48.29 45.40 43.71 43.96 41.68 50.19 39.68 28.39
[33] 34.14 35.07 40.66 38.39 42.14 22.94 36.46 19.91 25.91 38.00 38.26 45.78 42.45 23.85 48.72 33.20
[49] 36.39 33.91 35.36 45.11 41.90 40.99 49.50 41.26 21.76 35.23 40.65 30.33 44.89 38.23 44.64 45.45
[65] 34.25 35.27 24.77 43.69 34.73 30.68 32.31 23.00 40.21 36.37 37.36 37.25 46.40 47.31 34.55 40.30
[81] 41.76 56.55 34.33 46.54 38.93 39.30 26.27 39.34 49.40 44.49 43.20 37.51 30.94 32.29 28.35 41.92
[97] 47.50 39.73 22.95 39.83 31.35 45.08 39.90 38.52 29.22 43.10 51.35 34.13 38.94 49.24 31.55 34.41
[113] 44.26 33.22 26.02 27.68 31.09 42.05 30.64 41.87 35.67 35.65 43.66 33.56 34.79 42.43 41.91 36.63
[129] 39.21 35.79 48.94 46.32 35.95 39.94 46.28 39.84 48.18 42.32 25.70 43.10 34.56 47.16 25.78 35.01
[145] 38.30 37.84 26.62 41.54 34.31 31.22 36.05 39.11 37.80 34.28 37.09 32.98 34.48 35.81 42.36 41.81
[161] 40.31 43.58 50.10 42.78 36.47 43.05 31.79 32.19 37.25 35.81 37.95 28.47 31.84 34.05 44.87 32.31
[177] 22.17 31.82 49.55 40.79 29.45 44.37 43.22 54.77 44.58 39.63 54.98 51.28 36.56 45.00 31.27 23.86
[193] 42.13 16.51 47.97 49.88 37.96 47.25 26.66 38.17 39.13 33.27 47.04 48.38 34.10 38.01 35.12 33.49
[209] 48.42 34.06 35.68 39.73 41.27 32.65 44.86 34.13 43.19 39.86 38.70 43.07 41.77 40.60 20.83 30.47
[225] 26.51 43.95 37.73 52.53 45.80 31.70 45.51 31.10 34.87 35.07 49.07 48.18 40.18 48.78 54.11 36.10
[241] 44.87 45.08 32.78 46.13 45.52 38.86 35.96 45.60 38.56 43.87 39.09 44.26 42.74 39.62 48.90 38.13
[257] 40.59 41.14 35.83 37.83 36.02 59.41 40.59 26.56 48.87 57.62 25.46 46.69 27.00 41.20 30.75 52.91
[273] 38.25 32.99 33.07 45.66 28.93 31.58 38.69 34.85 41.54 35.33 32.93 32.62 37.77 32.43 42.99 43.96
[289] 41.65 33.15 29.33 44.22 22.92 46.11 36.85 49.98 28.73 27.56 28.77 40.69 35.78 41.67 40.25 32.63
[305] 44.32 36.53 36.47 42.57 28.04 39.22 38.71 28.35 44.46 56.47 49.18 38.29 36.00 33.77 44.20 28.87
[321] 38.33 20.25 33.74 39.28 44.92 30.41 39.61 30.46 41.88 42.55 44.29 45.76 33.44 29.04 31.13 41.64
[337] 48.94 36.15 35.77 41.46 37.48 37.04 47.27 32.20 16.99 48.08 35.05 44.18 33.89 42.40 43.61 45.75
[353] 41.30 35.38 36.11 31.66 39.46 47.68 37.68 61.45 42.56 41.07 40.97 44.74 32.58 30.29 45.43 45.04
[369] 45.28 29.13 37.06 47.31 38.50 39.27 28.95 38.29 28.38 32.53 32.88 37.10 47.96 36.69 37.43 38.30
[385] 37.65 14.95 36.84 42.16 47.18 44.37 29.24 50.52 60.91 48.74 32.40 55.94 35.24 22.54 31.98 50.68
[401] 36.16 46.99 50.07 65.47 42.31 43.80 54.85 41.02 40.30 44.73 42.10 43.59 44.74 24.89 35.68 46.11
[417] 38.11 33.17 47.22 31.39 42.77 35.61 50.38 38.60 43.78 28.59 41.04 43.72 35.62 41.13 35.14 38.20
[433] 35.67 21.48 41.45 25.02 27.32 21.89 40.99 38.85 45.08 45.38 24.87 54.21 38.51 47.61 26.27 33.55
[449] 37.81 32.45 43.44 36.16 48.37 27.53 26.82 46.29 36.37 46.15 29.24 38.18 38.68 34.07 43.56 34.92
[465] 36.65 37.23 28.58 39.76 34.70 41.93 30.97 49.80 35.70 45.46 36.25 38.60 35.48 46.28 27.16 48.79
[481] 44.76 35.30 49.22 24.38 40.92 24.51 38.34 17.64 38.40 31.29 32.05 49.99 28.95 47.31 36.67 40.93
[497] 48.62 36.35 30.50 23.54 41.65 55.73 40.64 41.72 27.72 29.49 32.45 36.53 33.81 28.57 52.65 42.03
[513] 43.94 56.48 45.29 40.57 40.73 34.75 34.62 43.79 30.52 45.03 29.95 43.24 50.97 25.91 39.40 44.74
[529] 55.56 48.83 39.19 52.78 40.43 35.68 43.54 47.92 39.00 13.46 38.93 36.17 43.45 44.86 40.68 42.40
[545] 33.82 36.32 43.17 50.43 44.77 32.03 51.67 61.58 47.73 38.09 41.48 35.58 31.74 34.11 45.76 50.52
[561] 31.57 42.43 34.31 27.12 43.68 34.47 34.07 47.49 35.66 44.30 41.57 47.56 44.57 45.33 39.81 38.78
[577] 45.16 29.17 49.81 33.69 31.10 33.70 21.47 39.75 40.98 41.29 54.30 46.65 26.27 47.22 41.44 47.06
[593] 39.80 20.43 32.55 38.84 37.60 40.24 34.51 26.43 27.94 29.46 38.60 44.64 48.43 37.27 42.21 37.36
[609] 34.78 32.67 46.53 24.63 40.92 23.19 42.74 42.69 55.94 45.20 42.93 37.34 21.24 48.06 38.81 36.74
[625] 28.50 34.66 51.64 24.11 42.70 33.69 49.61 31.12 35.94 42.95 18.01 41.22 36.51 22.29 37.28 25.30
[641] 34.58 33.80 38.33 45.29 35.00 39.46 37.98 19.67 47.55 34.04 38.66 45.94 34.64 36.24 56.24 38.48
[657] 54.77 42.68 39.54 50.82 25.67 31.31 38.02 35.85 37.64 35.74 43.49 46.70 28.46 20.87 28.36 37.71
[673] 49.02 42.97 38.95 35.06 38.55 42.40 46.68 45.46 42.76 38.94 33.30 47.20 27.99 36.12 38.43 11.95
[689] 39.01 30.01 55.49 41.69 40.52 42.66 40.36 37.47 20.48 32.64 42.45 44.04 33.86 41.07 45.17 39.06
[705] 43.55 37.45 50.01 37.62 35.11 28.81 49.75 33.99 37.93 28.95 41.33 37.26 36.87 34.66 27.85 44.85
[721] 36.13 38.09 30.13 43.01 33.32 41.97 50.49 27.40 24.47 57.25 34.06 41.25 46.13 41.11 31.00 42.54
[737] 46.76 46.99 38.37 42.79 38.55 49.12 30.07 47.29 48.66 43.13 45.55 50.96 39.54 39.35 38.00 31.27
[753] 37.19 39.71 35.44 35.33 39.82 32.21 40.59 33.77 48.85 39.61 37.05 33.57 36.57 39.76 48.75 38.77
[769] 36.73 38.05 39.74 39.14 37.44 43.66 31.98 28.20 38.18 43.10 38.99 32.14 37.49 28.45 33.25 42.86
[785] 42.48 43.77 42.55 58.05 39.02 37.26 33.41 31.23 23.13 23.93 42.19 33.80 44.15 40.78 32.70 45.06
[801] 30.47 50.78 46.63 32.26 46.39 42.67 39.61 37.64 49.40 37.94 38.38 47.66 44.20 46.64 40.47 21.86
[817] 43.07 54.32 28.37 41.27 30.87 34.73 33.74 37.88 25.36 31.53 32.07 41.67 56.06 32.64 31.10 38.96
[833] 50.25 41.98 44.30 45.42 27.49 38.47 41.56 28.39 26.27 29.15 38.43 33.03 38.22 35.87 36.67 49.87
[849] 42.01 31.27 42.21 41.24 34.41 44.08 48.98 29.80 35.10 41.01 44.10 31.83 34.56 30.55 52.72 42.29
[865] 44.19 38.25 42.14 37.44 46.68 41.87 46.61 25.78 46.55 46.32 31.56 31.70 45.55 33.13 25.00 45.13
[881] 37.40 40.00 34.85 42.49 36.44 39.96 40.29 53.42 30.18 33.54 44.20 20.98 45.28 52.57 43.36 33.53
[897] 45.77 29.51 50.31 36.12 30.79 31.57 43.87 39.79 33.53 40.33 34.81 22.62 39.85 42.30 43.57 23.37
[913] 39.81 43.55 49.34 35.04 37.66 40.91 33.46 35.04 49.24 34.46 53.34 48.62 44.94 29.69 23.52 49.73
[929] 23.62 36.56 42.11 46.46 45.90 32.35 30.47 42.73 49.06 50.56 20.70 53.77 28.34 28.86 32.33 41.39
[945] 46.06 41.84 29.00 42.23 40.92 36.82 43.34 46.56 44.96 31.56 30.28 41.69 41.21 41.71 52.94 42.60
[961] 46.08 34.73 40.12 32.32 42.53 26.66 46.65 38.65 36.72 52.23 41.33 46.19 29.88 30.67 24.31 39.51
[977] 19.77 48.66 34.62 38.46 41.11 30.98 46.41 37.11 37.51 52.23 20.03 33.07 41.52 56.64 21.57 47.38
[993] 33.74 52.68 27.14 18.89 44.79 54.51 38.42 42.86
[ reached getOption("max.print") -- omitted 7000 entries ]density_gram <- density(pst_gram)
plot(density_gram, main = "Density Plot of pst_gram", xlab = "pst_gram values", ylab = "Density", col = "red")
pst_gen <- y_posterior$Gen
pst_gen [1] -0.336259 5.421016 -2.070452 -3.864327 -2.329377 9.599332 2.341612 3.888579 0.195945
[10] -0.988255 -10.482393 4.726151 -10.735708 4.812651 0.613476 -15.421126 7.458396 -4.367548
[19] 3.511281 7.294645 9.841403 9.990754 -1.180291 -3.691716 -9.741715 -6.627670 -5.137832
[28] -0.848989 -10.407494 -6.197579 -15.935158 -5.605109 -9.054800 0.987422 -3.819461 -3.991178
[37] -7.966152 -5.786592 -0.008239 -10.442185 -5.798388 2.378973 -6.005132 -5.070985 -10.575037
[46] -10.217830 -13.407330 -16.773603 -2.616412 9.115410 -4.355538 -10.593461 0.872785 -13.431707
[55] 8.038806 8.276597 -3.279344 -5.991877 -4.478842 -9.741289 20.220092 -13.947706 0.058628
[64] 5.310283 7.230607 -7.122810 -8.723101 -10.102857 -9.986747 -14.460826 5.285884 -5.258822
[73] -23.001661 -0.110396 -0.230776 4.044957 4.845807 7.577506 -10.588604 2.924012 -9.978517
[82] -0.611442 -10.188708 -11.057308 -2.218713 -11.259309 -4.327092 -6.016482 -7.908411 6.043780
[91] -3.457963 -11.819914 -6.936388 -8.807306 -13.852171 -2.320043 -8.308554 -4.839289 -3.626115
[100] -5.470007 -9.724511 6.483118 -9.606888 5.468177 2.192224 -2.272653 1.146615 -8.998758
[109] -9.547409 -1.648923 -10.509915 -0.867274 4.734686 -5.956585 -10.486839 -8.223798 -5.317020
[118] -1.761636 -2.197854 -14.496968 1.895192 -3.609850 -0.691089 -1.329228 1.859751 -0.850321
[127] 3.082366 -4.601353 -5.486166 3.466837 5.326725 4.752155 -5.161996 6.136557 -4.138717
[136] 2.902578 -14.815336 3.719221 -1.080444 1.615780 -0.756766 -9.213037 -9.905852 1.042570
[145] -2.194072 -4.470309 -0.384314 2.931838 -7.280097 -9.708149 -16.727450 3.891873 -0.391361
[154] -12.215520 9.142799 1.675985 0.536396 -9.714720 -7.990414 -9.127843 -3.971838 6.090579
[163] -6.049193 2.922225 0.985855 -3.408628 -9.722008 -2.318991 -4.309190 -1.024400 1.532153
[172] -14.351198 7.219340 2.448024 -8.178926 -8.942536 -15.651781 -7.239871 -8.041255 -10.989651
[181] -5.221224 -4.209503 -9.244022 8.657323 -13.747497 -3.722136 -3.460394 3.092181 -2.266351
[190] 11.052833 1.861565 9.756665 2.954138 0.015717 11.221877 -3.376741 -8.858695 -12.884660
[199] -6.217670 -1.183323 0.992734 1.752425 -10.377984 0.092506 -3.936242 19.095561 2.547117
[208] -3.200126 -11.509861 -8.259332 8.138252 1.107799 -0.062468 7.605979 -2.878756 1.674620
[217] -6.951288 1.807621 7.247213 6.302034 -14.518420 -7.739977 -7.133773 -7.593429 -6.854018
[226] -6.878293 -7.157974 -1.858156 -5.613795 -10.181565 -5.897846 6.099591 -8.374010 -12.892262
[235] 6.981454 -6.266595 -5.178673 -16.330671 -9.065957 -18.787914 -6.725170 -11.151132 -5.366024
[244] 5.917880 13.226704 4.898431 3.104295 -0.733569 -13.392922 -0.361732 -3.648410 -7.267860
[253] 12.704164 -13.308319 -5.315701 -4.833460 0.499770 -12.044276 -4.927664 21.041837 -2.122881
[262] 1.513560 -2.438550 2.510813 -3.732281 6.017305 -3.431176 11.495216 5.138604 -13.016560
[271] 0.085832 0.011009 -3.057723 -17.943717 -0.865185 -2.433680 3.844409 -5.962113 0.562913
[280] -1.689453 6.389848 0.472891 -8.526555 -19.406529 10.686556 -3.178385 8.536322 3.091768
[289] -9.508803 -2.913570 -1.051159 6.647423 0.644260 2.490714 4.889970 -10.632756 -7.185918
[298] -8.321120 1.132598 -12.573792 -9.430872 -9.413185 2.038796 -2.418698 7.177597 -5.380919
[307] -4.994889 -8.003778 -8.113601 -9.660847 -6.552602 -3.885869 5.574066 -5.838091 -0.835261
[316] 5.071017 -0.426455 -0.816985 -7.374792 -3.641848 -13.805133 3.906461 -1.748363 -11.278552
[325] -1.336404 2.247451 -4.762906 -9.126644 -3.003583 -2.531765 -11.627186 -10.586453 -12.351023
[334] -6.310643 -1.544622 -8.859113 2.006791 -6.782818 -3.922657 -10.060834 -10.288379 -5.108049
[343] -5.556675 -2.728294 -0.691989 -7.404462 1.461574 -15.486168 -4.040345 1.275892 -4.839467
[352] 5.582496 1.870991 -10.945154 6.035028 6.615273 0.033980 -1.712664 5.928935 -6.709352
[361] -5.013635 -5.460083 -15.548563 0.643574 11.822351 0.592219 -6.085920 3.631806 -13.145286
[370] 1.066228 -3.605192 -9.138485 12.505291 -10.529564 -3.897962 -14.133241 7.829986 -2.043486
[379] -1.568830 -0.140820 -7.918661 -16.303969 2.086900 -5.008017 -4.762888 -3.205484 11.430613
[388] 3.350996 -5.672120 -2.282673 2.577225 7.769017 -6.739366 -6.693238 0.240406 -2.745810
[397] 5.525949 -6.204752 2.753149 8.897074 0.054514 -5.123753 -5.316051 -7.852384 5.046129
[406] -0.349686 -0.089750 -7.706524 -10.840908 -7.127448 -0.724264 -9.376372 2.825296 -14.257384
[415] -4.026594 6.668552 2.101860 -9.356290 -8.883704 -7.734120 1.441323 -16.421712 -1.519138
[424] -18.770932 -10.279811 0.309338 -15.683323 -5.870601 -0.089618 -8.222879 -11.994334 -9.402575
[433] -6.290123 -5.889831 9.221920 2.329413 10.664019 5.435983 -7.626526 2.330151 -3.417962
[442] 13.386851 -2.737955 -3.633300 0.082573 -9.859663 3.344967 -3.964731 -10.367991 3.167621
[451] -4.510495 0.930472 -3.558075 -2.059570 -3.020554 10.767542 2.904050 5.861457 -4.422559
[460] -5.461730 -6.421791 -8.800073 -3.533588 -7.136701 -7.678167 4.777818 -5.220223 -15.172452
[469] -5.082663 -13.866035 -3.002074 -9.838850 -12.721553 -16.420930 -5.145851 -18.427130 -12.064579
[478] -4.937791 -4.413706 1.185228 -15.474649 -11.623900 -5.772791 -3.211692 -10.425600 0.637551
[487] -1.093399 -8.043331 4.159968 -12.963382 -9.302864 0.693651 -10.580614 6.308634 2.602516
[496] 5.780803 -5.878647 -4.778002 -11.124135 0.490276 0.635072 -18.268293 -7.397223 -2.213956
[505] -10.404636 6.548354 -0.663647 3.099890 -10.524805 -3.044989 -1.673535 -10.526193 -12.206890
[514] -3.959823 -3.224963 -12.303659 -4.882121 11.791215 -4.246273 -8.087787 0.311195 -1.793512
[523] -5.257716 -1.578111 10.526340 7.124213 -6.867733 -7.909121 -5.243312 4.636888 4.015280
[532] -5.189137 -9.769807 -1.886358 0.318543 1.753459 -3.440635 3.242442 -7.207645 3.891741
[541] -10.241986 -1.300800 -2.123658 -8.561048 3.956822 10.063113 -11.863912 -1.988541 2.217851
[550] -11.643598 0.947872 2.377501 -2.072088 -16.979405 4.237909 -6.248691 -4.908682 -11.274428
[559] -11.109068 -10.247177 -10.807653 -12.084922 -1.860680 -7.814641 1.583978 -6.294620 -3.704085
[568] -2.677218 -4.712909 -11.650945 5.762367 -11.653761 -3.170230 -7.505287 -6.379574 -5.873133
[577] -9.605467 13.053028 11.248985 -1.943644 2.665236 -2.815999 -5.433507 -1.815993 1.496122
[586] 14.782780 -6.448585 3.101977 2.948170 0.652649 -10.732502 -8.039938 -16.897107 -10.051772
[595] -17.735048 -2.720669 -3.265224 1.878355 2.133142 -0.192888 -8.664137 -4.712270 -6.522238
[604] -7.035999 1.522853 -4.367934 -0.709598 -8.265768 -0.959071 -7.124068 -1.374159 2.677996
[613] -0.084338 -0.897388 -0.423820 5.044638 -2.504798 -6.926689 -9.853938 15.441779 0.559376
[622] -5.787111 -4.611835 -1.949169 -4.244757 -9.307143 2.895162 -7.232328 -2.817908 -18.422490
[631] 6.954249 4.850912 2.228307 -2.762239 -2.224826 -9.025058 2.874764 -0.587615 0.677173
[640] 0.252267 -6.381958 0.298355 -3.498125 -11.145297 -16.624314 0.223214 -6.799687 -8.600359
[649] 4.232203 -0.369426 -0.012786 4.052832 3.373167 -2.427460 -5.169458 22.240377 -14.338425
[658] -7.484807 -8.888818 -2.149348 5.686771 6.222934 4.292096 -8.156881 -4.905346 -8.174250
[667] -11.235034 -7.489959 0.673323 0.699940 -6.228764 3.349266 -6.726206 -1.518387 10.887462
[676] 3.733590 -2.190056 11.482970 -6.392277 -7.853837 12.742159 4.848498 -14.188902 18.887564
[685] 4.626860 2.001671 -4.638811 -2.880858 -3.037511 -1.519940 -12.281872 -0.321047 2.471579
[694] 0.252273 7.127941 -7.363657 -2.699759 0.033462 0.211019 -0.933889 -4.583647 -11.602200
[703] -6.246194 12.946906 -4.279646 4.150455 -8.373680 -18.110741 -2.275151 -10.871130 2.536159
[712] -6.897010 -3.709795 -10.371767 -4.353305 -9.372623 -3.328026 7.562994 -18.352838 -3.725053
[721] -5.894845 -6.070735 -6.866515 5.597946 3.541493 -15.277935 -0.714392 4.517807 -15.756803
[730] -5.332781 -5.738092 7.477545 5.842889 -14.668083 -10.507044 0.921243 -5.171982 -14.365022
[739] -4.512105 -4.597606 -3.458893 2.230619 -15.313013 3.457920 3.877492 -2.579793 -10.236007
[748] -4.312396 1.315565 -2.509448 -5.823834 -3.789558 5.658204 -3.740385 -9.994625 0.289183
[757] -18.198976 -2.117052 0.379055 -10.760162 -2.642569 8.541824 -7.981574 -6.642612 -11.236099
[766] -3.030748 4.340423 6.854069 6.880042 -7.588970 -1.450887 -3.323773 -2.033044 -11.896962
[775] -12.137599 -2.185407 11.527898 -1.990899 -1.149890 -6.607528 8.949967 -5.322564 2.025328
[784] -5.924485 -12.547526 4.356636 -3.206642 -12.668430 -4.482629 -10.962337 -9.015587 -6.396233
[793] 0.649867 -5.341951 -0.544155 -3.588094 -1.569619 -8.391832 -11.064584 4.296106 -11.442264
[802] -10.375211 -1.583640 0.513151 -10.992682 -5.933961 -9.307166 -8.249038 -6.164651 -2.210997
[811] -2.529593 4.410264 -27.331451 -10.632110 -0.626608 3.339534 1.238447 -2.256733 -27.693039
[820] -1.748364 0.490824 2.245630 -2.153223 0.657139 -6.640139 1.656372 -0.351088 -11.242928
[829] -0.548513 -5.275882 -4.585528 -4.674580 -5.336262 -3.802008 -6.706245 -14.855536 -11.439570
[838] 10.909850 -0.489173 -9.429571 -6.930507 4.036630 -20.486067 -21.252817 -8.012113 -1.781713
[847] -14.819506 3.731105 -2.562823 -0.711222 4.877506 -2.908695 -4.808512 -0.304009 -3.662441
[856] 1.504528 -11.888605 -13.701576 0.945294 -2.642560 -1.183030 -9.609107 -10.631757 -0.369818
[865] 10.928827 -6.035046 -7.571440 -5.871314 -12.546406 -3.327989 -11.370841 -0.262228 -7.066307
[874] -0.782537 -9.715518 -2.098000 -0.886800 4.450389 -3.577947 -0.573379 -1.845207 2.862648
[883] -7.543040 -5.304020 -16.416821 -7.116444 -4.882347 -2.202597 5.437394 -0.669122 -10.578645
[892] 3.686107 -13.922494 1.335694 -13.964651 -5.439115 -8.585105 -1.833396 7.282172 -9.805231
[901] -9.498753 -1.199273 -1.495989 -1.276519 14.819276 -10.985668 -1.958360 -1.087211 1.644404
[910] -4.154147 5.349412 1.357512 -10.704006 -3.254556 -6.822688 -4.190733 -2.175990 -2.356131
[919] 3.305140 -6.217610 -2.479293 -1.604580 9.682701 -6.927220 1.277452 5.872328 9.717050
[928] 1.411570 3.655255 -4.242744 -22.034471 -0.834294 -3.205084 -7.545228 -7.526234 -15.037676
[937] 7.715554 9.053072 5.487070 -1.913270 -8.318529 -8.327044 -2.076590 1.458385 -0.208224
[946] -6.297460 -16.019301 -14.842226 5.339955 -5.230499 -8.618249 -11.922304 -8.313197 -4.995109
[955] -10.273642 -5.026583 -10.264537 0.415520 -0.005511 -6.925420 -9.498108 -12.780217 5.950772
[964] 2.694525 -9.317935 2.075808 -2.486301 -11.055316 -8.006491 -13.613982 -14.305346 2.703369
[973] -2.779209 -2.589081 -1.827941 0.616089 -2.297181 -2.177360 13.976722 4.708046 -2.939251
[982] -2.878822 6.233753 2.406556 -8.536830 -10.289464 0.601656 -3.159437 -3.099671 -11.047786
[991] -6.438881 3.012653 -12.948514 -4.206152 -8.424725 3.987734 -3.897745 -8.679267 -3.194952
[1000] -6.259942
[ reached getOption("max.print") -- omitted 7000 entries ]density_gen <- density(pst_gen)
plot(density_gen, main = "Density Plot of pst_gen", xlab = "pst_gen values", ylab = "Density", col = "red")
pst_lex <- y_posterior$Lex
pst_lex [1] -28.9784 -48.8464 -48.6659 -74.4814 -39.9429 -48.3171 -57.9452 -53.0551 -67.9843 -32.7947 -48.0880
[12] -22.1473 -4.6382 -54.1656 -54.2418 -34.8009 -49.1298 -40.7403 -58.0540 -39.4102 -35.4156 -29.7107
[23] -43.0456 -33.3631 -59.1739 -55.3473 -63.8869 -56.3093 -52.3142 -44.8831 -31.8300 -42.8867 -39.5746
[34] -60.0639 -42.5531 -26.0382 -53.7627 -8.1613 -24.3729 -17.3964 -23.3687 -42.9663 -61.8065 -50.8497
[45] -40.9928 -31.5663 -57.0513 -64.9309 -68.7596 -31.3802 -16.3857 -47.7228 -62.1695 -36.2341 -82.0391
[56] -61.0224 -55.4615 -59.5408 -45.8443 -42.4482 -54.7654 -56.8329 -40.4590 -60.8457 -46.8965 -22.3780
[67] -51.5869 -75.8099 -52.4062 -74.0965 -52.2400 -49.0775 -58.2055 -18.7304 -58.0830 -60.0659 -70.7579
[78] -53.3362 -48.2014 -30.7423 -70.5273 -73.0290 -45.0579 -52.5530 -39.0295 -40.3220 -6.6425 -50.4010
[89] -42.1585 -66.1515 -31.5218 -57.1437 -52.6457 -32.3696 -77.8307 -43.2744 -56.5244 -47.7086 -50.9040
[100] -52.5079 -56.0727 -55.3685 -26.1110 -31.4530 -37.6039 -35.7349 -61.6095 -49.8232 -45.7617 -52.9481
[111] -65.0072 -42.4583 -34.4074 -51.7705 -32.6369 -46.1656 -22.5146 -37.4077 -27.0119 -60.8437 -68.5764
[122] -36.8392 -34.5667 -54.1139 -47.7997 -60.6075 -46.1492 -53.2043 -29.9170 -23.4286 -50.5434 -26.3755
[133] -35.7918 -64.6998 -39.3265 -37.5534 -22.5184 -46.8131 -38.1172 -65.2626 -18.0268 -18.3047 -48.1816
[144] -51.7322 -22.7189 -47.9134 -46.4580 -62.2738 -51.1022 -41.1968 -57.0066 -19.3754 -60.8222 -63.8411
[155] -27.2622 -50.8919 -28.5352 -75.3474 -55.6171 -61.7040 -17.9785 -32.2734 -55.1498 -40.7742 -49.0225
[166] -56.4983 -54.2088 2.7606 -62.0358 -57.5506 -37.1763 -39.2469 -72.0128 -50.7302 -43.9567 -55.5483
[177] -61.5541 -23.8100 -71.1487 -47.7458 -59.7675 -99.4391 -47.5164 -43.1114 -30.9776 -69.1521 -44.8970
[188] -38.6891 -43.7506 -56.5714 -50.0970 -23.8125 -25.7381 -63.9534 -43.8607 -31.3135 -62.2373 -42.6637
[199] -32.3645 -26.5889 -47.2981 -27.0559 -35.0705 -54.1000 -30.7968 -74.6078 -55.3254 -36.5379 -50.4122
[210] -40.4320 -53.9291 -60.3227 -62.2652 -38.0795 -73.6476 -54.3904 -16.4484 -34.0917 -58.2071 -58.1659
[221] -27.6025 -48.6308 -27.5435 -41.1779 -31.6309 -21.9580 -63.5180 -15.2432 -47.6551 -50.9665 -50.4306
[232] -72.3619 -67.7943 -44.8980 -42.2692 -39.6455 -59.9869 -21.3759 -35.1168 -63.4726 -69.6429 -34.3614
[243] -36.3644 -34.3067 -48.2227 -57.8380 -56.9317 -48.9925 -3.3359 -39.9114 -48.1312 -56.2780 -18.3854
[254] -37.7972 -29.6002 -42.3780 -13.2385 -78.4219 -38.0517 -92.2381 -13.8458 -65.5922 -42.0447 -27.4635
[265] -41.4781 -78.2570 -70.8704 -42.9243 -54.8011 -68.3926 -54.2189 -66.4858 -35.9515 -62.4033 -38.8859
[276] -37.6137 -41.6559 -40.9528 -56.0782 -46.9772 -25.2767 -47.2714 -20.4864 -4.5802 -36.1391 -59.1391
[287] -46.5341 -45.8537 -63.5543 -63.1922 -64.0906 -72.6739 -32.0521 -36.9963 -17.2504 -39.7410 -31.7366
[298] -70.8326 -64.9302 -42.4879 -39.5246 -38.7591 -54.8130 -45.8022 -62.7265 -35.3236 -40.8772 -56.8053
[309] -28.4380 -45.9468 -69.6221 -27.0037 -45.4053 -52.8309 -47.6559 -39.8191 -53.7140 -57.9904 -83.9634
[320] -37.2964 -63.0051 -42.1295 -77.4016 -42.1952 -59.0143 -47.9888 -43.2383 -45.1163 -32.4543 -23.3728
[331] -59.7675 -41.3630 -52.8709 -43.6518 -32.7486 -50.9338 -51.4025 -48.9997 -46.8638 -32.0118 -34.5111
[342] -41.3383 -44.1903 -44.5899 -16.4941 -35.4117 -33.2140 -52.1811 -61.5815 -32.5321 -41.7927 -50.1412
[353] -38.2818 -39.0862 -60.9361 -31.8912 -67.8725 2.5573 -43.4386 -50.8297 -25.2769 -78.9892 -37.6562
[364] -28.6232 -45.4607 -53.2057 -46.9439 -60.8771 -40.1829 -32.4409 -23.2265 -45.7180 -58.4582 -36.9545
[375] -50.2705 -16.3533 -57.5844 -40.0986 -37.2508 -26.5117 -29.0828 -61.2382 -48.2142 -28.8898 -64.6334
[386] -40.2095 -22.2026 -47.2689 -49.3410 -61.5622 -55.9906 -20.2921 -66.0966 -47.5200 -45.6359 -55.9871
[397] -40.6498 -79.9705 -48.7511 -56.8398 -56.8929 -48.3449 -56.3779 -11.3221 -55.5884 -47.5442 -46.1509
[408] -40.9607 -54.0206 -69.7419 -44.0592 -45.5884 -41.5302 -59.3424 -17.5159 -16.3349 -61.3657 -44.4908
[419] -31.3686 -47.7015 -72.5286 -19.5696 -71.5192 -45.4764 -33.1640 -60.1328 -33.8915 -53.8983 -45.9301
[430] -20.4616 -62.0677 -33.9923 -52.4632 -51.6153 -81.0496 -58.6994 -25.5493 -27.2170 -58.1879 -48.8188
[441] -27.4844 -66.6259 -76.3550 -61.6366 -58.4629 -59.5681 -63.7940 -78.3381 -46.3427 -43.0267 -57.8263
[452] -35.1177 -36.2780 -43.0006 -39.9058 -53.0689 -52.4827 -57.0942 -41.7765 -49.2382 -6.5016 -28.2714
[463] -56.2775 -65.9396 -76.2089 -52.8770 -38.3796 -23.8384 -9.2932 -51.6291 -43.7374 -37.7674 -15.8919
[474] -40.7552 -57.4728 -40.9373 -47.3183 -27.1886 -67.2576 -59.2804 -56.3030 -43.0716 -43.5776 -26.7201
[485] -31.0644 -55.8398 -24.3682 -53.7986 -52.9868 -25.4133 -48.9819 -56.8035 -70.4822 -54.2384 -67.5527
[496] -63.8996 -38.6424 -42.5835 -33.7094 -66.0069 -58.6579 -20.0931 -60.2014 -47.8324 -75.5950 -52.8223
[507] -48.3168 -33.6582 -50.5841 -36.6467 -83.6442 -60.7052 -64.2673 -59.1637 -35.4241 -53.0595 -61.0668
[518] -55.6317 -75.1830 -70.2986 -40.1128 -29.9268 -48.7326 -10.0446 -58.1771 -52.2003 -46.9433 -29.7474
[529] -43.0970 -81.6457 -42.3257 -38.2701 -58.6381 -38.2115 -38.7622 -63.6869 -65.7102 -31.5055 -41.3869
[540] -79.8349 -7.7417 -56.0968 -41.0272 -41.5611 -57.7897 -72.6570 -49.1237 -39.5166 -55.7729 -33.1530
[551] -78.9948 -10.3650 -50.6461 -66.7662 -31.3740 -49.1929 -53.4004 -43.9548 -65.1506 -42.9533 -53.6842
[562] -56.3222 -27.8292 -38.8316 -42.6855 -37.4694 -47.9717 -60.1875 -41.0529 -16.7713 -33.0195 -24.7517
[573] -41.7432 -75.4658 -72.3419 -61.5613 -63.9681 -40.4091 -31.3154 -56.7771 -48.8843 -56.1070 -65.9857
[584] -27.4552 -74.0113 -21.2363 -31.9419 -51.8302 -52.5924 -45.9460 -49.8789 -53.7730 -23.9816 -25.5993
[595] -44.8476 -76.2606 -53.0117 -9.1730 -19.2331 -33.4916 -32.8716 1.5991 -69.3964 -22.3104 -49.8355
[606] -63.4589 -33.5658 -73.5012 -23.2802 -43.0454 -31.2704 -22.6395 -46.6249 -47.5839 -60.5269 -34.4997
[617] -57.3566 -63.8527 -42.7551 -20.1365 -47.0733 -18.1632 -81.5471 -42.7499 -52.3330 -47.2908 -51.7209
[628] -58.3883 -53.2710 -23.8522 -40.7875 -39.6735 -58.2886 -61.5118 -50.5434 -26.1583 -51.7632 -57.1068
[639] -29.2980 -62.8619 -26.5933 -56.1771 -31.5166 -58.1049 -50.3175 -58.3218 -64.8691 -52.9069 -55.6022
[650] -12.2732 -79.2374 -43.6154 -38.1620 -53.9810 -33.2270 -62.2463 -40.8734 -29.7133 -27.4666 -42.5192
[661] -57.5553 -57.7248 -39.2309 -65.7226 -64.1557 -66.5807 -60.5698 -49.9388 -50.5718 -20.6438 -46.0346
[672] -50.6823 -41.9241 -43.4991 -50.2306 -38.8378 -60.8491 -42.8522 -31.4606 -18.6048 -60.0479 -49.4672
[683] -46.6660 -97.5817 -19.1757 -33.9994 -82.1649 -53.3446 -37.6726 -12.4544 -55.0835 -37.0400 -24.0872
[694] -35.4847 -73.3366 -30.6902 -36.1618 -46.5191 -76.3363 -15.7446 -45.2504 -51.6229 -86.8014 -33.9769
[705] -66.9848 -25.3010 -64.4380 -46.1889 -47.1887 -52.2388 -32.0120 -46.7049 -21.6026 -58.1921 -37.0749
[716] -43.7320 0.3159 -26.5868 -21.7685 -51.9372 -22.6049 -29.9407 -36.3897 -25.7312 -50.4457 -35.6543
[727] -40.9632 -43.5020 -10.2513 -52.8279 -12.7225 -43.1980 -19.4441 -40.7771 -27.1007 -36.8641 -43.5199
[738] -12.4249 -18.9560 -31.3255 -52.2504 -44.4392 -62.1820 -52.0239 -71.4287 -24.2586 -33.8851 -63.2818
[749] -82.2694 -45.3079 -23.3593 -56.8680 -38.9808 -60.5030 -54.7535 -54.5708 -60.1583 -68.4213 -43.5838
[760] -57.2149 -52.6049 -58.4670 -45.7643 -44.0127 -38.0033 -56.6568 -33.4472 -44.9987 -43.1393 -37.3633
[771] -35.1194 -32.1802 -73.9632 -19.5728 -38.4634 -24.0586 -71.3746 -37.4011 -61.6437 -36.0926 -61.8039
[782] -29.5818 -40.0871 -39.2438 -86.5686 -50.1873 -57.0894 -46.1400 -33.1190 -61.9365 -54.7465 -7.7994
[793] -59.8099 -47.0245 -47.3099 -34.7706 -38.1221 -26.3219 -37.4225 -46.6122 -27.1250 -66.2748 -20.1099
[804] -53.9471 -66.4616 -53.8838 -44.8390 -52.1157 -20.0068 -37.2206 -49.1530 -29.7206 -36.6539 -70.9588
[815] -66.7235 -47.8615 -28.3909 -45.7743 -27.9220 -54.7617 -54.6988 -47.5585 -54.4603 -36.2785 -42.1418
[826] -34.9467 -16.3053 -11.0089 -58.7069 -48.9455 -56.6498 -54.7384 -28.6062 -18.1325 -66.4964 -20.5924
[837] -38.7652 -57.5220 -20.4264 -36.1317 -57.5981 -4.4110 -50.6138 -37.8860 -45.6221 -59.0096 -74.1251
[848] -42.7322 -28.2268 -46.4383 -54.8069 -42.4450 -57.4603 -28.3973 -43.0943 -38.0123 -17.6828 -52.1603
[859] -35.2787 -33.8872 -8.5433 -54.4864 -71.6543 -23.4217 -70.8560 -86.8485 -41.5390 -11.5702 -43.2081
[870] -55.2893 -54.7594 -63.8185 -46.6554 -74.0936 -56.8596 -26.1396 -60.2982 -13.4989 -20.9027 -11.8357
[881] -50.2213 -55.3923 -30.4494 -49.7939 -40.7392 -67.2094 -57.2483 -54.2784 -27.1308 -31.5282 -62.6907
[892] -47.1592 -34.6357 -42.4748 -48.7905 -50.8117 -59.9573 -44.7202 -69.8463 -14.3379 -20.0656 -52.2570
[903] -22.6820 -50.8503 -55.8745 -51.2371 -52.4351 -47.4203 -36.1433 -53.5402 -68.0331 -7.3697 -55.3364
[914] -62.7637 -40.5478 -24.6937 -43.7957 -20.0956 -46.6609 -30.5675 -31.5002 -38.3047 -48.0107 -29.9340
[925] -51.6090 -18.0960 -48.0767 -29.3699 -4.7991 -52.5382 -44.3430 -12.4570 -68.6330 -39.7352 -17.7631
[936] -30.1121 -31.5038 -37.0354 -53.0928 -60.2398 -30.5177 -51.5079 -59.1942 -55.4052 -33.8782 -47.8367
[947] -44.9996 -40.1110 -73.9918 -54.4412 -30.5024 -55.0213 -43.3797 -32.8785 -39.3628 -40.4416 -44.0316
[958] -39.9926 -45.7505 -37.7986 -34.4473 -49.7366 -59.0971 -53.8776 11.7043 -38.7647 -70.9813 -35.0141
[969] -47.1937 -69.3786 -63.9308 -54.3442 -13.5827 -62.9035 -39.6561 -66.6570 -57.8323 -46.6185 -38.6158
[980] 5.9377 -57.3744 -34.3530 -56.9128 -30.2741 -56.4504 -44.4584 -72.5113 -46.7703 -45.5445 -39.4245
[991] -57.8440 -78.5168 -58.1716 -40.6264 -38.0933 -18.5861 -47.8490 -49.2881 -58.1686 -56.7038
[ reached getOption("max.print") -- omitted 7000 entries ]density_lex <- density(pst_lex)
plot(density_lex, main = "Density Plot of pst_lex", xlab = "pst_lex values", ylab = "Density", col = "red")
pst_synt <- y_posterior$Synt
pst_synt [1] -24.35131 -52.69917 -20.76281 8.89170 -10.47833 -39.53073 -14.65562 -30.80821 -45.18107 -45.43449
[11] -43.02262 -12.68632 -19.43547 21.75472 -24.52983 -28.34679 -13.36468 -32.29432 8.43637 -24.73748
[21] -52.88441 4.53085 -25.96000 -32.17645 15.44621 -15.26178 -36.06610 -5.59934 -30.94408 -31.87567
[31] -6.97469 -12.50490 -18.28703 -10.31003 -9.49701 -43.87841 -26.03715 -27.71712 -62.60267 -49.71388
[41] -41.57826 -7.32022 -12.09175 -18.97589 -40.26837 -20.87660 -22.41765 2.39376 7.14876 1.39583
[51] -47.26142 -35.71709 -0.70272 -44.02984 -11.78579 -18.72828 -25.52016 -35.14329 -22.67467 -25.17291
[61] -0.50657 -2.65407 -1.73057 -9.26134 1.04842 -17.43226 -20.29612 7.15463 -11.48298 3.89165
[71] -10.03687 -17.17986 -12.71297 -32.31703 -2.80811 -28.09789 -1.88708 -26.21944 -20.51237 -29.76852
[81] -8.11528 -13.50990 -20.38195 -6.95576 -17.96194 -24.52757 -19.47918 -10.53702 -23.40539 -21.22797
[91] -24.50250 -14.92554 3.00214 4.64755 -7.20061 -25.54565 -20.42301 -11.08873 -21.64976 -25.50318
[101] 11.13058 -16.11606 -13.07275 -20.15522 -7.69137 -21.86173 -27.17182 -16.76477 -24.33522 -21.25146
[111] -42.13555 -7.90353 -20.64216 -27.41937 -9.33038 -25.19000 -3.47618 -47.23524 -39.03591 -5.99887
[121] -35.29561 -61.85269 -16.31523 -33.06003 -40.04740 -8.68174 -0.62181 -61.65123 -41.66083 -53.48208
[131] -27.03090 -30.69510 -45.09963 27.12286 -40.67376 -23.67652 -52.32280 -42.34535 -7.13526 -9.01643
[141] -37.87415 -48.36485 -19.72608 -20.40798 -38.40346 -25.16232 -12.50700 -3.36188 12.43114 -33.25953
[151] -27.73813 -50.37212 -23.89048 -19.81560 -24.65750 -13.78283 -27.35446 -1.99263 -20.17228 -9.22648
[161] -16.73277 -24.39275 -10.78803 -28.02217 -22.58134 -4.68893 -33.03993 -20.31110 -13.83125 -31.93079
[171] -7.50587 -37.00663 -0.76904 -24.99494 -31.57197 8.80152 14.55934 -34.23282 -22.64780 -20.60818
[181] -26.83139 28.99298 -5.64255 -36.52907 19.21302 4.38446 -35.26799 -43.19494 1.33225 -58.77524
[191] -3.11285 -23.55510 -26.84706 -20.70460 -59.21539 -46.30264 -45.32562 -28.72835 -31.41872 -6.45785
[201] -11.37352 -29.27702 -23.72231 -10.96955 -28.64547 -21.86596 -32.23255 -20.41633 33.62041 -61.07884
[211] -18.81839 -31.42549 -15.49383 -0.34064 5.35521 -31.85330 -43.51691 -33.38458 -7.98612 -22.36017
[221] -31.09804 -60.83112 -46.22059 -36.25814 -25.01913 -29.37346 33.17562 -39.25451 7.63121 -10.00130
[231] -36.43561 0.60660 0.73191 -36.24088 -3.29584 -4.65505 -16.58725 -36.69077 -50.63362 12.77286
[241] 21.94846 -38.69097 0.04184 -16.79344 -35.01369 -34.77301 -20.50019 -13.10262 -13.36356 -37.63734
[251] -60.92641 17.73297 -31.45399 -34.28495 -27.09484 -24.20261 -25.23063 -14.04545 -4.49223 24.26257
[261] -6.32531 -37.71085 -21.45111 -43.90957 -34.20970 -6.62407 1.18734 -25.73293 -13.93953 -20.74891
[271] -1.85090 -19.61512 -27.35403 -5.38823 -16.44750 -44.28207 -23.81744 -30.30841 23.15980 -13.06967
[281] -6.62368 -21.04634 -13.95191 -39.42531 -23.63905 -10.96155 -16.06486 -35.90460 -3.25266 -21.68159
[291] -27.33205 -10.31259 -21.04046 -23.19851 -20.38271 -20.52724 10.32047 0.53199 -4.05690 -37.01743
[301] -18.11837 -33.14022 -29.86419 -17.91400 -31.62731 -46.57273 -12.21205 -46.09870 -30.56472 -24.74691
[311] -23.87835 16.01377 -17.45532 -11.95324 -19.14936 -12.20489 2.75872 -21.70901 -11.22982 -27.65555
[321] -13.39586 -15.49027 -19.80345 -8.97868 -25.74272 -13.11433 -23.22414 -31.65440 -11.54780 -35.00340
[331] -10.90765 -34.37972 -28.51202 -24.35955 -26.60262 -12.16501 -20.99941 -20.18446 -13.17888 -47.16572
[341] -43.49894 -36.26677 -54.38828 -27.82011 -15.54964 -12.19502 -44.93739 -16.51760 -19.34187 -12.04002
[351] -9.46549 -42.83512 -11.86695 -44.37837 -25.74988 -19.53021 6.37822 4.52173 -9.53726 -12.36464
[361] -45.20222 8.44316 -33.39023 -24.79680 -49.26174 -33.55441 -27.02360 5.40203 -14.75932 -33.14036
[371] -27.74808 -26.24840 -6.70973 -18.81239 -16.56273 -15.84590 -1.39521 -9.84160 -35.78880 -38.00686
[381] -26.96687 -21.84446 -14.01041 -26.89055 -47.55926 -26.31825 -46.66904 -22.78997 -34.36908 -43.08690
[391] 15.83545 -26.51927 -24.63361 -6.38446 -14.56413 4.92922 -38.91560 -11.17122 -1.84148 -20.84209
[401] -13.56062 -9.62860 -6.10217 -44.68162 3.99802 0.41179 -26.94150 -33.91520 12.17826 -6.13837
[411] -7.48761 -22.49120 -50.44970 -19.67265 -30.13812 -27.15335 -35.78490 -13.77279 -14.52755 -23.11338
[421] 7.99573 -43.30904 -15.47218 -27.78527 -44.33184 -31.09581 -29.19707 -20.33022 -7.23501 -18.73779
[431] 5.25491 -23.83976 -44.09833 -35.27371 -17.48516 2.24223 -47.96688 -15.32165 -5.00824 -16.21291
[441] -38.48260 2.39252 -4.15525 -8.07520 -20.27009 -39.20920 -14.87781 5.59844 10.00646 -5.50369
[451] -6.07113 -12.47786 -53.38887 -35.91870 -2.08767 -19.99054 -39.06654 -30.70088 -28.44251 -38.75415
[461] -14.97053 -13.54555 -19.96294 -6.68634 -16.24230 -17.96254 -23.60348 -13.05144 -28.97323 -41.67291
[471] -43.94751 -11.37182 -29.56281 1.02630 -23.48838 -17.04101 -24.55288 -5.35796 -11.98875 5.98980
[481] -14.60721 9.28268 -33.91872 -27.87320 -3.86112 -25.25144 -25.32270 0.20617 -29.66612 -44.40420
[491] -57.97369 -5.36438 -4.95824 -13.53727 -12.52840 -25.20109 -15.13518 -51.19278 -22.60861 -18.20870
[501] 11.19257 -4.63097 -21.09910 -18.27261 -7.36220 -24.64910 0.64761 -28.48468 -17.19355 -24.56598
[511] -3.43624 -19.89127 -11.46586 -0.40689 -29.78073 -4.24026 13.13451 -18.01409 -3.27022 -15.09920
[521] -40.75504 -11.97323 -27.71885 -49.78997 -57.66022 -25.40543 -19.49406 -36.32147 -46.75500 -38.76536
[531] -12.99420 -31.59686 14.98321 -11.11627 -35.79368 -5.56559 -11.10526 -42.11933 -22.40724 -1.06548
[541] -39.28853 -16.53822 -36.02337 -57.86113 -12.41474 3.88645 -1.13554 -26.58744 -31.84920 -16.52263
[551] -14.37394 -62.27317 -15.81127 3.28754 -28.98940 -15.61139 -35.76431 -30.44422 -37.08764 -25.42892
[561] -15.91742 -12.16047 -35.25078 -31.54451 -33.20939 -17.39785 -32.93305 -24.78752 -36.27819 -13.54057
[571] -46.67527 -36.68015 -19.46591 -15.16711 -23.47302 6.94022 -18.71276 -28.13631 -19.46064 -1.83809
[581] -15.22395 -34.30397 -2.18986 -32.78756 3.10229 -37.45208 -7.63015 4.77363 -42.91472 6.17501
[591] 0.07548 -16.47234 -10.28799 -19.44233 -19.45921 -47.02399 -4.55208 -8.49014 0.90934 -28.86146
[601] -24.72674 -22.32137 2.33850 -37.19286 -3.02854 -12.89593 -19.76881 20.52432 -24.12840 -4.74084
[611] -16.98166 -38.95492 -26.52929 -29.26171 -28.44603 -39.40880 -11.40694 -39.05061 -18.87705 -38.06207
[621] -30.55623 1.15500 -11.92701 -31.25059 -24.40530 -28.16242 -3.42314 -28.30652 -17.33691 -50.89484
[631] -11.72195 -32.21901 -39.40549 19.13042 -22.34903 -26.11761 -29.47105 -9.30030 -18.53750 3.02518
[641] -22.34358 -10.81746 -33.57657 -22.34866 2.84860 -26.36589 -34.25748 -10.32650 -7.07460 -36.79128
[651] 3.93782 -38.94444 -42.10226 -23.39433 -16.02512 -15.09043 -35.89840 -13.89460 -31.03753 -7.51150
[661] 4.81620 -23.78919 -3.39755 -17.24389 1.59250 -6.94309 -20.97250 5.69577 -9.81385 -21.10706
[671] -27.58972 -33.98208 -20.78815 -21.29318 -9.40457 -66.54288 -24.51641 -33.04812 -23.64883 -21.75009
[681] 0.73540 -24.75086 -20.90443 8.41424 -32.71025 -15.91832 10.87985 -21.55321 -45.15703 -29.11600
[691] -9.21337 -24.25629 -7.90129 -13.12002 -12.99296 -49.78293 -1.23356 -15.96571 -23.50895 -22.69716
[701] -6.96772 -22.94041 -38.53718 -58.55726 -4.98132 -51.05118 6.16163 -11.72005 -20.19649 -9.84647
[711] -1.36153 -9.97492 -37.82198 -5.65952 -20.37201 -21.93195 -55.70008 -33.73665 -16.02173 -34.42705
[721] -31.47826 -32.04373 -13.37714 -23.23739 -21.31706 -8.57823 3.07223 -17.62472 -44.97780 -28.59966
[731] -12.74834 -19.16190 -40.06595 -51.02756 -34.49882 -58.09728 -8.59807 -54.93950 -31.26724 -8.80499
[741] -15.20951 -31.89230 -10.19734 -13.06538 -46.58920 -52.78970 -30.90480 4.65841 -23.64628 -4.34041
[751] -21.81306 -21.70252 10.58018 -34.62581 -26.66712 -17.48641 1.19595 4.31790 -26.98086 -6.77289
[761] -15.77848 -15.53447 -36.00233 -16.41273 -18.38031 -21.45494 -16.38114 -25.10203 -5.69489 -3.23795
[771] -29.35990 -32.27348 -18.99448 -31.47673 -50.61456 -18.81009 -25.86430 -29.64113 -5.84481 -20.46607
[781] 10.37991 -25.57893 -43.78698 -13.32596 -8.23096 -37.00055 -25.35604 -5.86072 -27.29687 -0.48841
[791] -7.15930 -8.15663 -17.09932 -6.11027 -4.12015 -13.08836 -32.91704 -39.53651 -42.24379 -35.65031
[801] -44.34649 -7.81423 -21.24295 -17.10757 2.28514 -32.98070 -24.85497 -10.65769 -7.62813 -17.57094
[811] 15.70680 -18.17921 -44.11096 -19.42440 -52.73989 -7.53145 -6.01140 -9.33237 -44.30548 -18.90400
[821] -19.48719 -22.17985 -30.20951 -22.97606 -25.19150 -33.60446 -33.17107 -68.76233 -14.44526 -16.14729
[831] -5.93924 -38.58683 -21.24243 -44.06328 -21.16556 -39.71380 -1.36448 -3.13649 -38.63809 -19.11934
[841] -31.65152 -18.05493 -41.77041 -6.25583 -24.19724 -43.72714 -18.44537 -58.72406 -40.21309 -40.22242
[851] -2.14782 -45.42743 -15.19872 -19.89354 -6.10678 -0.18041 -44.78073 -19.24607 -0.06810 -17.78125
[861] -58.30230 -10.40872 -17.00294 -40.76609 -2.68085 15.53831 -43.90669 -7.30956 -35.35776 -39.33276
[871] -22.35506 -23.53042 -56.25923 4.17221 -26.87756 -11.73811 -5.55146 -27.46072 -24.55612 -35.07348
[881] -13.33364 -23.49070 -4.12418 -13.92962 -19.85207 -6.99353 -28.98018 -39.48200 -40.31791 -53.81739
[891] -0.91816 -11.14657 -32.91532 -10.23921 -2.23213 -25.20885 -7.41430 2.41450 -17.26597 -33.62661
[901] -29.31389 -23.75694 -20.26744 -21.56927 -16.24884 -1.93749 -14.42621 -21.46492 -41.17690 -15.36296
[911] -16.51955 -22.94950 -21.62614 -36.96359 -9.26122 -6.01828 -15.91384 -16.59503 -19.45556 -49.89155
[921] -50.66363 -23.16990 -13.23625 -22.25945 -17.87204 -21.32124 -11.17846 -31.42424 -25.25672 -2.98681
[931] -17.26051 -21.10476 -10.26911 -15.68548 -9.09234 -17.50595 -32.33365 -26.40359 -7.86356 -14.48394
[941] -29.91467 -10.94372 2.08461 -18.15117 -23.45498 -24.35419 -14.60036 -27.93869 -15.50684 10.61345
[951] -49.63467 -6.67226 -8.09188 -13.84087 -30.95266 -9.65144 -20.93771 -4.46800 -16.25365 -2.21627
[961] -32.12069 -35.34032 -22.25100 -15.62396 -19.53970 -36.55948 -18.25886 -14.21196 -19.78934 -28.57425
[971] 3.67296 -18.26578 -28.77689 -18.17131 -32.69687 -27.45477 9.62470 -0.27869 -45.87856 -45.83430
[981] -14.59774 -30.05189 -16.20796 -59.15906 -29.55102 -23.09375 -8.28151 -12.00420 -42.39101 -34.32105
[991] -15.14867 -13.08025 15.79383 -28.95502 -20.61047 -30.68996 -12.08274 -18.35118 -9.04691 -22.70079
[ reached getOption("max.print") -- omitted 7000 entries ]density_synt <- density(pst_synt)
plot(density_synt, main = "Density Plot of pst_synt", xlab = "pst_synt values", ylab = "Density", col = "red")
pst_gramxlex <- y_posterior$Gram_x_Lex
pst_gramxlex [1] -11.08280 -6.08654 -17.04219 7.76664 -0.32583 4.98926 -6.99529 -10.58977 -3.74560 -0.06514
[11] -14.54703 -6.99544 -7.76189 -20.65629 -10.50493 -25.90487 -16.71278 -12.14217 -23.89411 -13.22386
[21] -12.33252 -8.29381 -25.32766 -13.02649 -15.50731 -3.01404 -2.11874 -9.37160 -6.76210 -3.80437
[31] -13.22596 -3.07535 6.93447 -8.82166 -14.84416 -26.26701 -6.71855 -14.27618 -12.41271 -3.00698
[41] -15.29811 -30.89310 -6.75717 -17.76977 -4.55855 -22.03309 -9.04184 -7.12720 -7.09147 5.42318
[51] 2.00446 -16.94352 0.76287 -18.71466 -25.31994 -7.64947 -8.89003 -11.03067 -0.23393 -10.10203
[61] -13.79092 -16.92184 -2.07346 -7.45063 -0.12170 -10.34707 2.00036 -8.66828 -9.85124 -6.47480
[71] -13.62523 -21.69970 -3.53309 -4.87064 11.31213 -11.17397 -2.14618 -22.81392 -22.28687 -8.22641
[81] 2.75818 -6.15772 0.73978 -12.87762 -4.52259 -18.13325 -23.21772 -2.60416 -3.05531 26.35757
[91] -4.72542 -5.36157 -9.26975 4.34086 1.29011 -5.75697 -8.61314 -9.19346 -13.30402 -2.99624
[101] 3.25198 -15.18022 -10.33424 -1.67478 -15.24370 -5.16032 -12.20625 -19.69192 -5.68986 -13.97159
[111] -11.50351 -8.54170 -2.48600 -15.68126 -15.11498 -11.83791 -13.79225 -0.48374 -7.03722 -14.26591
[121] 4.94865 -11.63486 1.83680 -4.15059 -8.16602 -10.52790 11.48945 -3.44841 -12.39657 -3.66313
[131] -23.99062 -6.53397 0.07796 -26.06522 -4.59210 -13.59520 -16.17098 -11.41517 -12.86337 -9.67061
[141] -21.87614 -0.15224 7.54642 -7.45027 -10.49930 -25.43459 -9.41680 0.76855 -7.62924 -7.02371
[151] -3.29645 2.78191 -25.80960 -34.28899 -19.14774 -13.17859 -23.42572 -5.28418 -18.46439 6.54554
[161] -11.96944 -10.17661 -3.85109 -8.14928 -4.16733 -14.89737 -7.30955 -30.15883 0.03682 -7.56035
[171] -5.52645 -1.38173 5.18603 -16.90540 0.68987 -14.38552 -9.73432 -12.54884 -14.68861 -7.31741
[181] -3.21254 -11.39522 -12.45271 4.82797 -21.54590 -25.72283 -23.77677 -19.23961 -25.50597 -24.93399
[191] -17.08541 -17.43908 0.60626 -14.86346 -3.11639 -12.97578 -6.71095 0.20881 2.13139 -10.07740
[201] -4.76982 -23.06568 -8.70241 -10.58015 -4.76932 -9.82113 -15.48771 -12.62444 -14.01491 -7.11118
[211] -26.78723 3.38389 -6.38233 -12.00129 -23.13706 -6.66229 -19.15944 -8.00509 -12.77143 -23.53392
[221] 0.25577 0.59464 -11.42155 -9.45258 -10.72829 -3.32999 -0.18340 -6.67195 -8.87881 -16.81241
[231] -28.60706 -17.87764 3.00827 -13.38061 -9.75908 -1.43186 -9.10538 4.22714 -15.64861 -2.31562
[241] -23.47033 -15.17662 -2.18224 -10.89571 -0.50625 -13.31124 -17.29188 10.54767 -39.29760 -10.02728
[251] -14.33420 -16.01155 11.89078 -24.36071 -11.71741 -13.88292 -29.94694 -3.07941 -15.57355 -6.28970
[261] -18.31991 -9.84941 -13.35457 -5.02919 -9.64328 5.57382 -18.82820 -21.22498 -11.94711 -25.92636
[271] 11.43854 -18.27723 -6.02673 -7.58474 -21.55182 -6.35768 -11.39573 -18.83192 -27.77062 -9.26056
[281] 9.07170 -4.44639 -24.38567 -11.12728 -9.92234 -5.04926 -6.94229 -3.32099 -12.33312 -16.10484
[291] -7.12478 -18.84269 -10.04503 -5.74118 -17.49090 -16.35831 -25.30949 -1.53215 3.48769 -4.35437
[301] -18.71864 -10.79530 -14.77440 -8.72793 -7.39130 13.60423 -5.74622 0.35518 -16.26410 5.24040
[311] -12.89293 -20.28800 -8.90661 -14.50790 -9.69912 -8.07590 -8.98401 -16.92210 -9.68588 -19.32864
[321] 0.87023 -15.25671 -16.37896 -6.72738 -20.63030 -21.07194 -7.14814 -14.55891 -11.02929 -17.41432
[331] 5.88068 -20.35850 -3.35870 -5.81921 -4.07108 -6.82461 -15.98549 -2.72462 -4.16649 -5.52780
[341] -18.91642 4.44987 -0.39758 -7.24134 -2.86723 -0.93993 -16.61832 -10.04806 -17.11601 -16.02527
[351] -9.89715 -19.83407 -9.85706 -4.74392 -10.99618 -4.04720 -3.18148 -21.03600 -1.06165 2.91694
[361] 3.88003 -10.95138 -2.78221 -23.25480 -10.62253 -13.12851 -7.05198 -20.19985 -10.59192 5.77002
[371] -11.63345 -6.15174 -8.37273 -9.25222 -9.88393 3.74481 4.39399 -8.53077 1.17318 -1.44057
[381] -26.80731 1.38498 -11.96895 -28.18535 -10.16310 2.64721 1.48176 -4.46050 -6.57647 -2.17727
[391] -10.54479 -12.90808 -15.48221 -9.02714 -0.50654 -6.71943 11.42145 -5.46784 -4.68606 -17.99849
[401] -17.73361 -11.85891 2.96250 -21.87981 7.00383 -13.57211 -20.16226 -21.66169 -4.62116 -18.21317
[411] -12.68783 -23.76064 -15.91971 -9.95856 8.76671 -16.07861 -19.61040 -5.66202 5.24045 -6.06803
[421] -13.48623 -12.43173 -9.88906 -3.61533 -15.43913 -17.29537 -17.69554 -0.35360 -12.26617 -10.04655
[431] -3.20959 -5.20022 -23.60164 -16.50657 -9.39616 4.16322 -12.18118 -26.71733 -14.25799 -11.25066
[441] 2.71351 -14.55656 0.24994 -1.96454 2.72647 -6.50379 -13.45644 -14.49271 9.14819 -13.36813
[451] -2.75456 10.54917 -6.67214 -17.76054 -14.27582 -6.26888 -12.17748 -11.48780 -9.13193 -0.89332
[461] -15.31045 -6.97502 -24.16436 -24.23144 -14.51255 -3.18041 -21.14783 -3.76852 8.75757 -2.26915
[471] -19.18211 -27.36611 -16.99069 -12.50369 16.68466 -4.55582 -5.79849 -15.20503 3.94415 2.15708
[481] -1.10498 -9.00407 -6.55428 -19.86286 6.07111 -18.77434 -20.86775 -13.14598 -2.76136 -10.77018
[491] -3.99846 -28.54187 -0.18223 0.17132 -5.10208 -9.21793 4.00227 -9.86344 -7.50296 -9.38078
[501] 7.72605 1.69071 -3.70008 -9.99601 -1.29277 -7.36798 1.12692 -20.63738 -4.13725 -22.50630
[511] -29.03635 -27.25299 -13.32766 -7.95343 -3.04714 -10.23562 -21.12549 -12.33987 -20.18263 8.16119
[521] -17.12236 -14.57439 -12.75331 -32.89061 -23.04698 -2.03969 4.93915 -15.85628 -9.72704 -17.26462
[531] -13.65250 10.49375 -1.33830 -10.87912 -1.59589 -4.47098 -14.30623 -16.50827 -7.36550 -13.57149
[541] -3.36353 -10.57110 2.44292 -8.80537 -19.07385 -19.45939 -20.67716 0.80730 -2.70263 -10.46810
[551] -5.83186 -9.20088 -14.26603 -20.80125 -6.07376 -9.26067 -25.36502 -5.26524 10.30146 -5.62392
[561] 5.65336 -1.10229 -8.66395 -21.90737 -12.36346 -19.83287 -7.19654 -14.83119 7.62498 -28.46925
[571] -0.18910 -13.53638 -16.54204 -8.18663 -3.97941 -6.73923 -8.20265 -8.09806 -10.59554 -0.08454
[581] -10.67756 -7.39254 -4.91080 -14.16270 -4.31856 -3.95288 -21.28618 -0.42638 11.23264 0.65814
[591] -6.00070 -22.18816 1.85836 -14.40884 -0.08279 -25.98121 -2.85354 -7.91139 -16.64449 -16.22455
[601] -0.37557 -15.31186 -15.81296 -4.69941 -9.68820 9.17577 -10.78678 -21.15587 -10.84749 -10.31658
[611] -18.94301 -7.13505 -3.71448 -13.88901 -19.30800 -21.34242 2.53289 1.25496 -8.81499 -11.81149
[621] -20.00478 -16.01828 -12.22957 1.94896 -17.84611 -10.43206 -7.65006 -18.23001 -7.59199 3.45741
[631] -12.32927 -11.50362 -9.55849 1.59153 -3.10258 -10.29148 -6.14636 -24.37288 -5.15046 -7.60405
[641] -10.45811 -6.53578 -15.13164 -3.03771 -19.15458 -0.04432 -11.99908 -0.32025 -27.78575 -9.06957
[651] -18.81634 -16.83028 -3.67537 2.48431 -22.87968 -5.85969 -7.63711 -11.61068 -12.37136 -2.56205
[661] -2.42483 -20.90540 -11.02077 -22.60569 -13.71824 -17.01228 -13.66280 -2.58627 -2.82543 -4.51321
[671] -22.92800 -18.20036 -10.96012 -18.02768 -17.65806 -11.50105 -16.90380 -5.78791 -8.77659 -3.09499
[681] -16.77349 -0.24174 -5.65577 -10.62662 6.11170 -24.06344 -28.58249 -10.33702 -23.65367 1.96860
[691] -17.09330 -9.72008 -22.09085 -10.88672 -6.13099 -11.65006 4.83747 -20.06165 -10.80636 -9.68036
[701] -9.52355 -2.57491 -5.87232 -22.61424 -19.87235 2.81585 -4.11223 -10.12017 -5.49102 -17.70410
[711] -3.74859 -16.25900 -8.52936 -32.68030 -17.32294 2.95291 -12.49487 -3.71006 -3.60520 -18.26989
[721] -3.78815 -6.99734 -20.71325 -10.54190 12.57897 -20.77675 3.41824 -23.91922 -10.52875 -5.52893
[731] -7.60805 -15.65198 -19.83230 -21.04243 -21.32242 -8.92506 -5.96735 -11.57556 -11.84872 -13.89458
[741] -13.78559 -20.70129 -9.67035 -6.17604 -11.67093 -21.28363 -6.10481 -9.76233 -3.87414 -12.38473
[751] -5.09527 2.69713 -7.18110 -25.37977 -30.79423 -0.43096 -0.50777 -12.90881 -24.11727 -13.27833
[761] 4.93520 -16.59259 -14.90532 -17.76146 -16.94168 -24.21714 -2.41745 -21.49588 -18.19651 -19.18167
[771] 26.62805 -3.90927 -8.83975 -10.85321 -26.27467 -23.11637 -16.30323 -15.00900 0.48189 7.94392
[781] -16.68108 -4.45070 1.71859 -26.44843 -5.58010 -6.17975 -11.41604 -15.13558 -8.16104 -8.63550
[791] -9.83710 -14.35381 -5.19395 -16.75213 -22.81257 -12.40660 -6.85898 -1.56773 -6.84348 -13.57445
[801] -8.64246 -23.95451 -1.61754 -9.82443 -2.63632 -1.39525 1.67373 -7.09858 -5.13594 -4.62313
[811] -5.32645 -13.12205 -29.59073 -15.42300 -6.17675 -8.52224 -4.44272 -24.87501 -16.46662 -6.73669
[821] -21.49467 -21.68990 1.56183 -13.03799 -31.00383 14.83748 -11.51240 -11.21921 -9.04493 -4.60955
[831] -7.25348 -17.48828 -13.47441 -11.69169 -12.49356 10.12094 -18.24067 0.83593 -8.77465 -20.56048
[841] -15.75145 -6.60663 -13.69602 5.53261 -10.97067 -25.88300 -4.68172 4.11551 -2.09063 -6.97928
[851] 11.54985 -24.14892 -12.99265 -15.05715 -26.35646 -9.49165 -17.55231 -7.54939 -20.51274 -7.29464
[861] 2.24866 2.40184 -6.88408 0.55948 -7.41537 -27.59101 -24.01985 -11.17612 -24.59056 -9.98298
[871] -18.94422 -19.27632 -5.69466 -6.43853 -11.47399 -15.06406 1.38486 -4.21842 -14.90408 -9.07737
[881] 0.19532 -11.35078 -19.56560 -9.25288 -14.64436 -25.56167 -26.14363 -4.03614 -10.80604 -1.22518
[891] -0.62535 -4.00090 -16.10513 -14.76173 -25.49890 -12.74796 -22.71360 4.39479 -4.62313 -9.62846
[901] -12.59034 0.55474 -15.82509 -2.62724 -19.19964 -5.90592 -14.00621 12.30464 -7.01888 -6.68624
[911] -3.50736 1.53301 4.75640 6.92010 -4.95960 -4.10576 -4.22065 -17.69833 -12.86365 -6.67419
[921] -9.60538 -9.87605 -15.24612 -26.54749 -14.44399 -5.50619 -12.49120 -15.58132 -3.07279 -5.67746
[931] -8.97394 -9.69684 -0.05886 -24.63628 -9.15756 -25.56488 -4.30014 -17.30616 -2.02380 -17.40122
[941] -11.90749 -4.83646 -0.11739 -2.17642 -3.16044 -16.99101 -14.28239 -1.04174 -3.40694 -14.29376
[951] -0.13732 -2.34037 6.57413 9.01424 -10.70834 -20.99154 -18.96958 -4.44755 -0.81690 -20.88665
[961] -22.00074 -11.59618 -20.25990 0.07973 -13.08686 -8.77299 -6.18519 -21.41187 -13.55368 -1.71078
[971] -5.08888 -12.10784 -19.59959 13.14861 -13.30264 -3.08658 -8.40427 -4.70066 -27.94099 -15.56069
[981] -7.88713 -7.30523 -24.39129 -21.29654 8.70596 2.21960 -4.43285 -10.80261 -3.24430 9.25269
[991] 0.04128 -6.02727 -19.69384 -8.11925 -13.66681 -13.55660 -16.52119 7.44623 -29.79066 34.80182
[ reached getOption("max.print") -- omitted 7000 entries ]density_gramxlex <- density(pst_gramxlex)
plot(density_gramxlex, main = "Density Plot of pst_gramxlex", xlab = "pst_gramxlex values", ylab = "Density", col = "red")
pst_gramxsynt <- y_posterior$Gram_x_Synt
pst_gramxsynt [1] -5.053453 6.187288 2.788221 1.284608 -4.870128 -6.332632 -3.613579 3.916330 7.211630
[10] 7.799748 -2.643932 -3.106716 3.015732 -1.024004 3.825743 4.259845 -10.983481 -9.339822
[19] 12.959679 6.541848 8.628574 -19.175502 27.641577 -1.544770 6.282753 -0.735286 -0.473835
[28] -6.199372 13.784847 -2.840982 8.067476 -7.237985 -8.564921 12.606500 5.555969 11.344557
[37] 5.943935 10.006514 3.015910 3.455928 11.921744 12.284612 0.308969 19.797172 1.918032
[46] 0.145443 3.852571 6.191142 1.228013 1.174749 0.247189 0.102489 -6.936929 0.878073
[55] 11.062615 16.392734 11.407588 28.730324 -4.287731 12.664581 4.504808 13.951294 -2.830624
[64] -2.165854 -21.729867 -1.512891 -16.866201 13.026079 2.497641 5.595442 -0.875782 1.781424
[73] 9.637527 0.631284 -12.888804 6.355983 9.994239 13.456519 -8.583225 4.242755 -4.019064
[82] 3.189612 -8.103400 26.863973 -9.095801 19.069311 -8.410639 4.441780 15.847373 -3.726269
[91] 6.071552 6.551298 -0.606627 -4.319927 -20.682455 5.685297 4.973333 1.660707 -6.152877
[100] -3.719542 16.713791 20.845728 7.773434 8.382857 14.904498 -0.605131 17.400956 9.785776
[109] 3.597514 10.888214 5.469694 2.483437 -6.577348 0.864946 9.195712 14.132052 7.406341
[118] -2.393484 5.079770 3.961647 4.042530 15.335220 3.774312 10.009882 12.582455 5.485260
[127] 2.045588 -7.816798 3.562309 9.432307 18.465854 -2.486680 -0.228495 13.930571 12.628623
[136] 19.392177 7.757399 21.378542 -11.067366 -7.494346 2.371555 -6.540370 -6.033924 7.914687
[145] 5.101072 19.699693 9.704806 -4.235995 4.355751 2.787770 9.549229 -4.539028 17.531405
[154] 20.284718 12.329194 -2.706850 8.241642 7.627439 19.492538 -23.548634 -1.004127 2.284058
[163] 11.915772 2.532465 -7.843447 10.366107 3.604952 22.241983 2.027551 12.019639 -2.149810
[172] 0.955109 3.482669 0.663005 8.796421 -9.734590 8.506266 5.484685 6.227016 2.697402
[181] 19.287776 19.861910 -11.175707 6.049371 22.623906 9.995620 -10.211583 -0.271583 4.162394
[190] 13.432062 4.207886 -2.112518 0.150159 -4.654543 -3.443783 5.245484 -2.259995 2.774097
[199] 7.813589 14.812771 -1.988457 1.994787 -3.995156 3.473761 3.508531 3.455824 11.349462
[208] 9.894377 5.403925 20.375832 19.608787 5.164285 -0.290915 -14.175791 2.050422 19.904954
[217] 12.788805 8.102365 0.287716 14.407251 -10.444507 7.054463 5.198207 9.477967 14.179353
[226] 4.684244 -4.266322 6.922544 8.215692 7.301607 20.094777 15.694471 15.993304 17.012609
[235] 18.260354 7.328896 4.283168 -2.291487 2.777631 0.775336 9.548955 6.559970 -7.379430
[244] 6.325941 -6.431213 21.182812 6.218338 5.947482 18.640357 -1.351808 8.462403 6.992952
[253] 3.664319 19.233249 2.899178 1.535741 25.139058 2.841079 1.213712 5.547729 8.783602
[262] 6.908939 -0.091830 3.224392 -5.788026 2.826116 1.519525 -7.765002 8.493097 19.977919
[271] -0.486242 9.558352 2.002735 7.675204 8.752569 -5.093942 2.877616 15.409736 10.103208
[280] 3.336653 -5.400225 -2.423008 -1.526734 -13.906285 16.009958 -1.162662 -4.959278 16.996247
[289] -2.019232 1.964645 2.759623 0.043366 1.327680 -8.706122 13.274514 -4.936797 2.659285
[298] -1.512589 -0.294874 6.404691 18.796568 9.428658 8.219358 8.899932 -3.546023 -9.899052
[307] 1.965884 9.535152 -0.351653 -8.208483 4.568598 3.492569 -0.430720 -7.955663 9.799567
[316] -21.958958 0.067696 27.516985 -2.688834 7.934065 12.272655 1.170769 -3.133768 -1.318990
[325] 9.569706 14.969196 -1.956410 16.811704 0.160913 6.049986 -4.029243 10.554711 3.105128
[334] 3.629912 9.884248 8.627549 10.816349 -5.030813 -5.997755 10.147918 15.785601 -1.311451
[343] 7.133458 2.989098 8.884431 -2.347602 14.892991 2.421884 7.163049 -4.874137 1.305174
[352] 10.627507 -4.155432 6.176532 -1.322800 14.024713 0.568358 18.763436 -12.333700 -1.006919
[361] 1.977944 2.437857 2.098135 12.671817 3.213519 -8.483142 7.966549 17.372031 -10.508921
[370] -4.235374 2.550647 0.448104 3.902981 3.124080 -0.993473 5.760251 -0.548848 -7.340884
[379] 5.584966 12.245191 15.573070 -6.598894 15.763066 23.671780 3.013174 -2.736038 -2.421748
[388] -5.660911 -4.268690 3.044785 2.422531 -1.704197 2.988869 4.077711 6.341435 -6.349027
[397] -2.251408 -1.811620 4.125453 18.976401 3.214969 3.074487 2.677881 -4.881523 1.833165
[406] 18.040605 9.751776 13.333837 18.255246 -3.189583 -1.991010 13.039830 6.144306 16.125345
[415] -4.124313 12.892118 7.829574 0.954264 -3.821196 10.606780 9.064349 2.475111 -0.347580
[424] -8.557615 7.055253 5.629817 27.635489 -10.543477 -0.741416 7.692615 -0.222345 -3.245987
[433] 3.267856 12.454894 9.440006 12.657657 11.303053 4.631319 0.902379 8.075658 5.818455
[442] 6.288345 -13.256888 1.737642 9.517268 14.521608 5.182836 16.173184 -2.360418 5.721045
[451] 4.986295 -7.084212 0.958797 6.155434 -5.333158 -2.041216 3.123794 14.143095 5.531897
[460] -2.620152 -5.702093 -4.931056 17.739409 21.886783 -5.449530 0.310195 21.781052 15.007412
[469] -4.696156 -1.432897 20.038847 15.436156 11.283308 -4.860775 -7.353670 4.733178 3.043015
[478] 2.799513 9.834391 8.391123 1.277970 -6.122896 -4.962989 25.508084 -7.717435 14.207182
[487] 17.838391 19.428274 -4.812144 -7.945381 2.251566 2.487607 -0.002364 12.629385 10.885681
[496] -5.232177 7.663254 19.422900 -1.059164 -8.704393 0.159366 23.095980 14.822701 1.832953
[505] -5.953754 12.345220 6.261403 17.237732 -0.734317 14.811900 28.598327 12.981526 -9.962202
[514] 8.513484 -0.889745 16.373108 9.158000 -3.408188 7.680220 -9.363283 16.090447 10.043217
[523] 2.746883 23.472436 12.805661 -12.517864 -6.557431 23.705527 9.005702 1.948991 24.532423
[532] -14.184604 5.817268 7.035089 -1.496171 5.874163 2.668661 3.817875 5.918923 4.632779
[541] 3.571793 5.316943 -5.335769 16.851643 19.601852 11.295398 9.688587 -14.697673 14.310540
[550] 3.202705 -12.199866 -8.082167 4.922504 5.447594 15.396581 9.833449 25.784216 12.717465
[559] -2.471140 1.368212 -12.856060 -1.141232 5.831997 11.831274 6.964628 13.222256 1.826584
[568] -1.803269 1.739197 -7.391400 -19.676821 1.418664 6.170360 11.433315 18.679056 10.429916
[577] 1.089153 7.766909 7.150399 13.463836 7.383111 -0.428451 8.986215 -6.312528 17.073858
[586] 25.841547 16.451061 1.439090 -2.660281 -0.967140 -6.694129 21.065100 -7.786669 -2.479409
[595] 2.377931 20.000263 10.938845 3.631645 7.303160 -8.328875 -0.654927 14.645951 6.273834
[604] 0.522520 -5.995467 -8.103050 4.813131 1.636077 1.362243 10.534795 18.323837 12.507997
[613] -6.871509 13.402546 13.536620 5.503037 2.175402 -7.231038 1.415712 13.186322 10.548349
[622] 16.419156 15.032837 -7.613518 6.620033 2.571275 6.914560 3.526747 2.140931 -1.237016
[631] 5.929109 -2.538458 1.444144 -7.651389 -2.286604 8.428135 -4.935518 9.211989 -0.014994
[640] -4.247242 8.357060 17.159924 4.456236 -2.946552 15.916110 -5.383668 2.688395 8.151128
[649] -2.700233 12.350878 28.148408 21.185762 13.165640 1.125888 10.159383 3.054255 7.305171
[658] 8.445932 6.896199 0.203805 12.987687 5.620717 15.626413 11.663436 6.877827 8.077279
[667] 4.668451 0.015477 9.984810 -2.084865 11.760378 16.255061 11.815446 0.786651 9.726244
[676] -2.323269 13.767656 -6.573214 13.470867 1.563920 6.301512 5.006630 7.239981 10.116298
[685] 7.797837 5.843406 19.191706 11.087234 25.614871 -15.980162 18.960213 12.199922 20.179291
[694] 9.843779 -9.570758 1.042982 1.193888 12.653550 8.809818 13.095755 16.630396 -3.519898
[703] 9.752238 15.176253 16.691420 -0.970269 2.526302 3.556606 -13.958326 11.634049 0.601352
[712] 0.513944 1.784148 6.963140 13.948034 3.989098 8.552681 -1.819524 -8.605711 10.568255
[721] -8.689244 3.296182 9.347971 9.579683 -4.996927 19.669514 8.214402 21.576987 -3.354634
[730] 16.862911 -7.265758 6.733549 26.113003 2.400829 18.369396 7.820513 -1.301266 8.568795
[739] -4.268840 0.641531 20.325749 19.461683 6.121038 -4.318052 12.716037 2.740823 3.760392
[748] 1.757774 0.017462 7.169518 12.714414 -1.683254 -17.584983 16.546308 30.359976 5.410195
[757] -1.328611 0.983844 17.968225 3.955490 -3.198009 11.150399 17.582791 11.933850 13.303401
[766] 3.346180 -14.141724 15.863480 14.580182 7.442435 -16.832083 15.571718 10.980115 9.057285
[775] 12.223758 22.166146 18.468949 5.159932 -2.596826 0.177323 17.546398 -2.340819 -10.739001
[784] 13.851711 8.649524 -0.676625 1.916790 -0.230504 4.486365 3.497703 -0.974030 -8.302877
[793] -9.957042 15.014614 -5.888460 -4.721529 1.183185 0.888209 16.190886 -1.503265 -13.911138
[802] 11.977848 12.100279 6.292232 -14.023810 -3.759534 -12.159657 1.121957 -6.947301 1.256539
[811] 10.867987 17.357463 8.429953 -1.639224 13.357072 -9.569509 -1.543032 19.295377 14.068746
[820] 14.481696 2.777179 11.448611 -15.947981 11.140153 7.911482 -0.914805 8.657599 9.579986
[829] -1.804865 -9.815804 7.580361 8.306247 2.065076 8.426913 19.803284 -8.565734 12.120181
[838] -2.147138 3.198560 7.143374 10.880711 10.227392 1.402729 0.764356 11.930294 12.809317
[847] -16.394427 2.641887 18.490961 14.833648 -18.337915 7.110666 4.970343 14.513695 13.699080
[856] -7.290834 1.049503 17.622133 -7.504536 -3.722740 8.276255 -5.210400 15.445376 5.436044
[865] -2.251778 7.757196 5.234596 2.733717 27.007857 25.268541 4.065656 11.640994 12.830218
[874] 5.452132 -6.976616 2.417533 -0.062211 3.390345 4.458874 -5.175640 1.479404 6.089584
[883] -12.033990 1.891364 7.365701 25.115510 7.663163 2.599787 1.056858 10.370422 7.933016
[892] 10.187097 2.440983 12.307552 14.815614 2.486874 12.888974 -2.736984 4.362567 2.402114
[901] 9.870973 -25.497400 3.613346 1.818221 14.448089 -7.433200 16.808825 -10.656762 1.349171
[910] -13.792504 11.097103 6.380533 -4.403192 5.192574 3.279144 9.336076 -14.284250 13.425168
[919] 23.041343 3.478552 6.070094 -9.637628 12.883459 8.688329 16.091607 -18.836069 13.398496
[928] 5.240835 11.189526 -1.539839 5.668806 5.575354 -6.911831 17.712669 -2.867567 6.120091
[937] -2.833317 12.702545 -17.544912 10.438811 -18.340939 -3.066357 -3.701081 5.496427 -7.708764
[946] 10.176771 -0.404156 3.491030 9.951289 0.125318 -0.950743 1.744051 -4.739348 -0.859759
[955] -0.234398 10.636048 2.310768 -9.338066 -1.976172 12.334621 19.533671 5.814421 20.861448
[964] -7.207150 12.383274 -2.055910 -2.315557 11.003435 -4.295582 -3.113120 -1.110859 10.263287
[973] 5.595698 -5.453013 5.098184 17.708365 -6.872907 4.302581 4.022909 12.102674 6.063307
[982] 8.300545 13.738784 -7.712485 -2.883992 -3.279923 0.957886 1.273057 0.522583 -14.027584
[991] 1.021488 -8.075856 14.415910 12.664000 13.644429 9.483391 -3.101986 -7.135397 20.942074
[1000] -19.067728
[ reached getOption("max.print") -- omitted 7000 entries ]density_gramxsynt <- density(pst_gramxsynt)
plot(density_gramxsynt, main = "Density Plot of pst_gramxsynt", xlab = "pst_gramxtyps values", ylab = "Density", col = "red")
pst_gramxgenxlex <- y_posterior$Gram_x_Gen_x_Lex
pst_gramxgenxlex [1] 0.620247 -14.802705 -3.031907 -12.518206 -3.485587 0.896144 3.107528 -9.625840 -13.807638
[10] -1.854760 -7.190642 -7.530326 0.490543 -1.693198 -5.949240 -12.643799 1.944658 -15.874529
[19] 3.689906 -6.660178 -12.844383 -5.479248 -15.729088 0.009928 -11.152186 -3.481186 -3.005218
[28] -3.888231 -12.468471 -6.519007 -3.542226 7.375220 -6.029863 -2.450335 -20.426812 8.361814
[37] 0.529527 3.802957 -6.831232 3.449239 -13.695477 -1.145402 -2.135542 -8.899339 -0.836994
[46] -0.771729 -2.103416 -7.061291 3.513063 -1.325883 -0.709435 -2.653010 -1.246442 -12.914394
[55] -7.805561 11.540513 -5.995020 -9.074787 -8.644881 -3.211989 7.426858 9.116633 -1.826742
[64] -0.390749 -1.556447 2.437535 5.455385 -14.371282 -11.949033 -0.511778 4.446197 1.861604
[73] 5.296238 -3.349326 12.734896 6.279348 -22.098260 5.885467 0.218703 -9.375201 -6.177203
[82] 7.697090 -3.380415 -14.354835 -7.561720 -13.500687 -5.518803 0.675018 -8.747222 11.838791
[91] -2.414711 1.920746 -3.534129 -2.534596 -9.506622 2.559798 -1.210134 8.740015 -3.730318
[100] -0.122586 0.848465 -1.815424 -7.862387 5.037356 3.227346 -2.880093 -13.556299 -10.254845
[109] -3.132556 -6.643010 7.716824 -0.550454 -9.380685 -21.776045 -9.193138 -8.522697 -1.903133
[118] -10.035891 -9.191380 -5.797914 -14.693970 -8.906908 -8.433130 -17.574949 1.157918 -13.687884
[127] 6.127547 -8.087820 -8.331359 -7.939487 -6.316624 -8.693443 -13.065221 -0.032911 -4.646264
[136] -13.532414 -13.042837 4.320586 -6.099963 5.181466 -16.534965 -8.978441 -5.049911 -3.781763
[145] -6.533067 -11.388316 -4.281225 -7.149702 -0.079692 -8.094843 13.883965 6.765797 -11.511591
[154] -1.593876 -9.263760 -8.760471 3.527212 -11.224924 -10.512672 -9.557601 6.804476 13.843517
[163] -3.770277 12.634594 5.994477 0.637912 -4.788076 -8.656035 -5.239882 -11.096487 -7.157884
[172] -0.940722 -3.697434 0.237897 -5.290765 -6.351777 1.743446 -17.383667 5.192458 -11.139472
[181] -12.132820 -2.259982 1.072937 1.658730 -4.300352 0.312188 -3.566758 -4.117766 -6.701533
[190] -15.218034 -11.657324 -2.316665 3.272414 -3.142184 -10.935175 -3.780663 -6.115197 -7.506022
[199] -4.673553 -4.892705 -11.357990 -3.414878 -17.888750 0.031883 -4.542737 -11.361098 -1.794212
[208] -10.322020 5.493849 -7.137931 -4.120643 -19.275419 -13.902299 2.775271 9.619169 -14.791475
[217] 4.955575 -9.697786 1.360200 -12.115047 -5.850588 2.633057 -3.563479 -14.094379 -15.521000
[226] -12.055339 -15.700789 -6.863448 -1.660808 3.405629 1.177136 -4.861643 -2.159526 -5.515356
[235] -8.027649 3.253884 0.832141 -1.020184 0.832413 14.346098 0.139533 -15.566387 -15.308158
[244] -13.857545 -0.760521 0.968491 -5.041949 -3.920126 6.415560 -1.559519 -13.264340 0.227163
[253] -9.847433 2.747456 1.009345 -6.985124 -7.978682 10.924207 -7.394256 -6.958987 -13.811044
[262] -2.468695 -0.589220 1.245843 3.451556 -4.446727 -1.401428 3.258065 -1.630284 -3.772275
[271] -1.073686 -1.971427 0.819016 12.077074 0.043719 -18.449167 -1.116916 -2.054095 10.499059
[280] 1.414139 -7.979170 -11.205719 -3.404366 7.540113 2.597013 -0.538856 -4.046045 -14.701842
[289] -11.756083 -2.314620 5.827013 -9.898007 -0.337264 -15.585084 -11.739887 3.904751 11.033448
[298] 3.674742 7.910148 -10.836030 -6.354638 -11.746723 -12.538644 -0.464538 -3.512302 -7.818940
[307] -5.347117 -9.747777 2.704066 -6.636801 -4.850151 -0.773624 -9.638883 -4.601407 0.338220
[316] -0.568073 -16.862695 -6.571378 -2.161010 -6.857514 2.674314 -2.807941 7.253726 6.861018
[325] 7.887984 -4.746066 -3.428606 4.525292 -9.257634 -4.931353 15.691386 1.859572 -9.213566
[334] 1.739223 -4.021720 -3.396908 0.154625 -6.964899 -7.912046 -11.010715 3.250941 -4.521081
[343] -11.078600 6.789434 -6.470014 3.413213 -0.918719 -2.771844 -6.666989 -1.341112 -14.986580
[352] 10.804664 -5.575929 -5.016929 -13.003689 -16.685012 -8.112235 -7.279620 3.898139 -6.951367
[361] 0.550031 -4.361964 -10.743320 -11.609663 7.678704 2.556417 -16.102261 8.165762 -3.306431
[370] -5.959541 -19.765213 -11.691467 -2.698805 4.660633 -1.332568 -8.533394 -2.648368 -3.944726
[379] 5.644252 -8.760918 9.131497 -2.281991 0.132333 6.395759 -6.148555 -0.205315 -11.693807
[388] 0.798298 0.297853 -3.428946 1.457795 -6.462318 -9.768840 7.230486 -13.353924 -0.558339
[397] -4.114742 -12.762576 1.326561 -0.582889 -3.092732 -0.701296 -4.695475 -5.043239 4.314954
[406] -6.798533 -14.609792 -2.832972 -16.915572 -6.464366 -1.062764 -5.423406 9.933683 -1.605776
[415] 8.854594 -8.552998 1.246873 3.981942 -17.461225 4.375500 -0.886104 -0.234374 -13.841314
[424] -14.584387 -1.061976 2.030452 -4.701442 -7.712958 -5.425253 -10.510626 -16.268405 -15.952177
[433] -2.722489 -16.425928 -14.459630 -1.417801 -1.403630 -8.529098 -0.173019 -0.901668 -7.299485
[442] 2.146924 -1.518157 -2.488474 -16.883947 -10.479583 -16.197317 -14.640758 13.534771 3.775491
[451] 4.781548 -6.435578 -11.065854 -5.890799 2.348578 -5.801838 -0.222637 -7.981581 -14.688852
[460] -2.648336 13.263132 -23.656847 -6.076678 7.334213 4.761573 -3.294466 -12.659610 -15.464786
[469] -19.903876 -1.075635 -2.510406 4.187656 -0.524530 -19.234291 3.769031 -3.038032 -3.170268
[478] -6.977247 -5.689943 -13.613682 -5.219825 6.138564 -21.760126 -3.512074 -2.348567 -6.430784
[487] -4.208613 10.787802 -3.597537 -6.235739 -0.255110 2.796509 -3.524978 -1.589955 -5.537785
[496] 0.100448 4.507430 -14.027323 -14.718673 -5.032210 3.978935 -8.415242 -7.598378 1.895633
[505] -2.688044 -6.428093 -4.079578 -14.655119 -5.042970 -2.398924 -8.967551 -9.880963 7.086559
[514] -21.782349 -7.402374 4.107432 9.831590 -6.017419 -13.817527 -10.089797 4.159070 2.309493
[523] -11.588469 -21.693938 -1.732658 -15.415267 -5.529209 -10.513573 2.553482 3.458083 -17.926387
[532] -13.505860 -2.921896 -10.185443 -18.835899 -4.003539 -2.009388 -16.684363 -10.872242 -3.111107
[541] -13.939475 -1.871000 -2.494939 -4.722964 8.201888 3.715337 -4.798800 0.258261 -1.044373
[550] -2.750095 -7.466510 -0.223540 -4.301676 -11.833573 -7.805759 -12.561109 -19.545580 -7.381407
[559] 2.678297 -5.632948 -2.656473 -2.637534 4.558934 -10.700316 -2.827868 -11.557701 -3.334698
[568] 0.891357 -10.445311 -6.289334 7.428145 -7.536621 5.639228 0.706883 11.864298 0.475986
[577] -8.269394 5.121606 -8.274295 8.340773 6.578011 3.540447 6.742491 -10.150563 8.266405
[586] 14.895748 2.355100 -13.025111 -11.977957 1.785317 -6.939888 14.294423 -6.984764 -7.309003
[595] -4.470453 -6.653162 -8.122173 -6.006874 -11.890401 3.719309 -6.045160 1.322056 -14.132577
[604] 12.173284 -6.271259 0.027098 2.807536 -2.469541 -12.838001 2.098926 -0.550622 -3.390257
[613] 2.293279 -10.542020 -11.961513 -7.940719 1.524430 -10.606955 7.423870 -9.847959 9.749779
[622] -4.108178 -1.253261 0.504294 -13.007417 -7.862513 -11.729238 -4.161678 -18.402110 -5.371164
[631] 2.952858 -9.793527 1.279108 0.111887 -2.708746 -6.389017 8.564700 -6.090507 -1.982940
[640] -0.056550 -12.234259 -6.980690 -11.297596 2.548561 -3.971944 -6.606962 -1.905842 -6.196378
[649] 6.947096 -12.748760 -1.698266 -4.434041 0.710141 -17.179844 -5.692807 2.992174 -17.428397
[658] -0.703328 -21.199391 1.820945 -16.522415 -7.330906 1.322582 -6.973663 -8.249427 5.551910
[667] -11.164655 -8.355179 -12.989436 -11.239530 -4.962034 -2.523621 6.680256 -4.346330 -1.784670
[676] -9.746036 0.650990 -4.858280 -13.398616 -3.227625 -20.024307 4.928818 2.051492 -9.795434
[685] 1.758038 -7.733281 -5.729714 -0.218997 -2.508496 -12.625296 -4.992942 -11.189414 -5.706230
[694] -14.084361 -13.014068 -0.065583 0.636258 -0.141185 -10.170749 -11.234265 2.953423 1.329771
[703] -11.147428 10.515368 -8.082523 -7.381537 -6.291954 -3.211842 0.015421 -13.156592 -11.252602
[712] -3.962140 -5.167692 -13.131789 -8.307483 -0.241687 -10.897203 -4.320337 -19.007264 -9.577169
[721] -14.657248 5.045017 18.912244 -1.727998 -3.725317 -5.275010 -22.654108 -23.193689 -4.315390
[730] -11.701336 5.594444 3.502378 -17.652708 8.043162 -4.120286 -7.030010 -4.659263 -13.517549
[739] 0.710200 -1.406357 -6.035634 -5.589002 -23.326234 -3.485686 -11.066429 -7.620707 0.700913
[748] 9.560767 4.243233 -2.213589 -4.511416 7.236957 -2.053329 -13.815921 4.200006 0.875215
[757] -14.897697 -1.585937 -12.997388 16.312842 -2.237487 5.670193 -10.471271 -8.923421 -8.081807
[766] -13.640058 -9.687109 -7.663081 -2.202857 -0.378754 -14.335197 -6.224559 -9.500599 -3.842366
[775] 21.742485 -3.833665 0.041075 -12.865040 -18.699726 2.257961 1.244823 -8.128887 3.039698
[784] -11.388324 -0.970902 -18.074340 -0.319527 -7.506121 4.585576 -8.159320 -1.078308 -10.832106
[793] -0.114377 3.316085 -12.055526 -16.876832 -11.402611 -10.071800 -3.832329 -7.967286 7.247344
[802] -8.872758 -4.576918 -5.058670 -12.051726 2.368051 -9.033369 -3.231642 10.854386 -16.697554
[811] 5.347236 7.596276 0.823617 0.481892 -15.743097 14.633736 -7.207215 -8.574181 1.891811
[820] 1.331926 9.720149 -8.724017 6.995028 -12.211293 -8.219172 -0.417589 5.553114 -2.569631
[829] -10.700535 5.074061 -6.909419 -7.107369 -2.292046 3.892487 -13.122155 5.295286 2.367691
[838] -23.526865 -3.160103 1.970745 5.672533 -9.851683 -1.593009 2.264230 -4.184854 -1.724977
[847] -5.957402 5.330426 -7.144102 4.831142 -5.056589 -13.649883 6.151373 0.349801 -16.250039
[856] 0.345436 -7.257334 -7.457765 -1.874905 -6.147608 -14.035478 -2.524430 2.267094 -4.610564
[865] -4.060788 -5.217743 2.820399 -9.386830 -5.061692 -6.484957 -7.391064 -7.197869 -0.749346
[874] -8.427933 -18.900975 5.392020 -13.569570 0.487969 -12.382680 -4.323395 -3.870834 -13.129241
[883] -3.146504 -8.023497 -3.402560 -0.553354 -1.378686 0.870250 1.184436 -10.125064 -1.750484
[892] -13.080093 16.033518 -6.960667 5.849638 -2.272554 -5.575062 1.845667 -9.831104 -14.875070
[901] -8.224598 13.417903 -7.788852 -1.262667 -9.791590 -2.572758 0.453520 -12.982236 -1.728957
[910] 1.275073 -7.771717 -5.098038 -17.639456 5.706840 -4.571080 8.393079 -6.789773 -9.943346
[919] 1.917187 -8.628855 -2.836402 0.030583 -20.947980 3.680629 -17.749991 -5.685082 -8.634569
[928] 11.445404 0.731374 -2.867261 -4.983436 -0.509161 5.714592 -3.955416 -11.137285 -12.551951
[937] -5.851357 2.959546 12.469716 4.355750 13.032723 -13.835589 2.937336 -3.664231 -5.912822
[946] -8.453695 -1.902683 2.053226 -4.158862 -17.211593 2.936978 -9.298263 -10.498156 -8.288327
[955] -12.637861 6.639447 -8.961019 -6.739150 11.067115 -9.019571 -3.377147 -6.240592 -24.084999
[964] -9.665404 11.307511 -11.462502 -7.081446 -5.074047 -6.726880 -15.785986 -7.846104 5.508417
[973] -10.285089 1.042023 -4.616004 -0.390450 -5.443724 -7.790874 -3.705020 -4.215645 2.809870
[982] -7.632529 -15.194150 -1.630142 1.825785 -4.753468 0.171565 -4.908247 4.886107 6.316514
[991] 3.432370 -15.116581 -5.825768 -10.343139 -4.566267 5.458148 -6.620336 12.883634 -16.788879
[1000] -7.662392
[ reached getOption("max.print") -- omitted 7000 entries ]density_gramxgenxlex <- density(pst_gramxgenxlex)
plot(density_gramxgenxlex, main = "Density Plot of pst_gramxgenxlex", xlab = "pst_gramxgenxlex values", ylab = "Density", col = "red")
pst_gramxgenxsynt <- y_posterior$Gram_x_Gen_x_Synt
pst_gramxgenxsynt [1] 4.79680 -0.66366 -3.95663 -4.53607 -14.82178 4.06552 -6.11669 -1.67869 -11.10258 -6.52136
[11] -0.25257 13.35644 2.60505 -0.46508 3.55198 -6.75014 9.43359 9.58306 -2.80645 3.93057
[21] -1.64812 -7.75411 7.18887 4.42505 9.85447 12.74543 -9.65229 -0.22764 -3.49437 -12.80035
[31] 4.19057 -12.34624 6.74215 1.19688 4.13037 -14.24327 -13.89912 4.96395 3.73231 5.50308
[41] -1.85232 -10.82491 -7.62924 -8.10522 5.43214 -9.51251 4.44598 -12.86420 7.68843 -18.03039
[51] 0.54491 4.68682 -21.63700 11.51606 7.53301 -15.12835 2.45170 4.69205 1.50995 3.68025
[61] -17.30413 -3.00069 -6.05521 3.53660 -1.28171 3.83999 -2.05078 2.27652 4.53554 -8.74292
[71] 3.06209 -14.22254 -3.87473 2.04190 -14.64583 -1.08543 7.59515 -6.37053 5.03760 4.11445
[81] 5.98911 -8.05926 0.85425 17.32783 -2.16369 -6.00804 -8.36117 -4.54220 0.10815 -3.33501
[91] 0.83863 0.82968 -3.56228 2.27058 16.12058 -9.89188 -0.88719 -12.44936 -7.86365 -10.02252
[101] 0.80040 0.45695 -7.85692 -5.55920 -16.45942 -5.03425 -2.28429 -3.32452 -7.38056 1.79528
[111] -20.40262 -8.00939 11.22045 1.97417 4.56876 -2.59250 0.88417 -1.52974 2.22934 2.44286
[121] 3.08602 -6.75664 -14.59565 -4.20762 -10.80472 16.42758 3.44389 -8.89697 4.43523 0.89889
[131] 5.21822 -0.44863 1.27385 3.91946 -2.55928 -12.59955 6.67246 -3.21958 2.44467 1.04457
[141] 10.48983 4.34259 6.24864 -0.52028 6.11994 -4.77286 -4.16854 5.03682 -5.83879 5.07564
[151] -0.81987 -10.70098 -2.86606 2.60383 -7.95193 4.86093 -4.83333 -15.83810 -12.07366 -5.20263
[161] 7.44624 -16.31818 -3.09645 -16.26770 -10.79059 -3.05172 -14.59105 -2.87476 1.08893 -4.14071
[171] 14.25532 -12.81575 3.44824 3.98420 0.24049 2.03289 -4.60779 -5.40431 -0.08470 3.37931
[181] 0.79936 -2.33568 -3.45137 1.38088 -1.48262 6.91184 3.42439 17.58672 -4.54290 14.16279
[191] 3.41243 -15.50567 -7.90779 18.78620 1.44027 21.15618 -0.08364 -6.43235 -3.23267 -11.37175
[201] 11.66050 6.41293 8.22507 0.80143 3.01132 6.56451 12.18455 11.04810 -11.89542 10.32697
[211] 4.92790 -1.01939 0.84898 1.48878 -17.26581 -12.26544 -1.69592 2.97373 -3.36433 13.38861
[221] -1.22144 4.56913 -6.18935 6.79547 5.88359 5.76383 4.95729 -2.77201 -3.85408 -1.69244
[231] 1.30238 0.04024 1.63689 -8.32265 9.19778 -9.07617 2.03961 0.90842 4.32316 -9.33032
[241] 7.10762 2.40833 0.33314 -1.06807 -3.46095 -13.33281 8.71181 10.53903 7.64933 -8.07725
[251] -5.74637 -8.68838 12.50080 -17.49129 0.38741 11.44297 -8.35917 -4.68873 0.78127 0.80676
[261] -1.00959 4.70160 8.45713 2.51500 -2.72119 7.64757 -4.76983 -7.26742 1.05975 -2.00104
[271] 0.59734 -3.54924 -17.41918 -10.93745 4.24897 20.16249 -4.38695 -0.30389 5.61923 -1.08673
[281] 5.73644 15.96977 -2.29246 -19.48236 -9.35489 1.23592 8.91882 14.62063 9.07823 -2.54926
[291] -6.45629 3.27365 3.47729 -4.23807 9.52990 -2.99231 -14.37449 -14.74917 3.16050 -0.72516
[301] -9.34162 12.01793 2.65525 4.21371 1.73474 -2.06606 -8.63286 -6.96728 2.49832 -3.37411
[311] -6.97259 -0.40974 11.99833 -1.03509 2.21754 -15.24405 6.60547 -1.55384 -3.62616 2.89877
[321] -7.88314 12.55398 -9.74817 -7.33908 -6.84247 -0.65540 15.06684 0.88349 2.55417 2.80819
[331] -7.80754 1.43274 -0.13708 6.27036 1.56301 -6.72339 3.07820 -1.86643 -8.11209 -4.98292
[341] -4.53138 2.54747 9.45418 -1.04300 -2.88764 -0.84772 7.28758 -7.35568 -3.62747 -11.94777
[351] 5.85402 -6.69356 4.42359 -3.06494 -2.09807 6.35945 3.08040 7.60341 -0.38057 -15.67417
[361] -4.01967 0.51797 3.93051 11.88993 -3.82948 -3.93018 -6.57109 3.48874 3.86306 -3.43056
[371] 2.90849 -4.74979 2.78482 -0.21962 -1.24498 -1.03178 -2.81645 3.17303 0.74535 -5.97158
[381] -14.38914 -7.55005 -8.72105 -7.75295 0.82800 -3.85458 11.13218 -13.45006 -2.15249 7.24786
[391] 5.70858 11.15627 -0.10056 -6.62993 -2.99426 3.70457 7.52683 9.13529 -4.75849 -1.11798
[401] 8.50455 -9.35037 1.89316 -11.36686 -3.09600 2.54759 6.05223 4.69461 -3.78791 -15.74405
[411] -11.24229 1.50943 -11.94883 3.73002 -10.81085 -6.92926 -14.72166 -4.68388 -0.86850 -14.42751
[421] 13.77758 -15.15254 1.60323 1.32665 10.84280 4.17600 5.54015 -1.92205 0.23147 -0.83056
[431] -5.33514 -4.37006 6.64579 -9.30778 -4.95591 -4.53308 4.75256 1.33908 4.61772 -8.62356
[441] 4.56283 -12.59240 -1.31703 -0.36174 4.60961 -1.07131 1.04040 -4.08906 -12.06296 6.30353
[451] 3.48078 -1.11259 -6.39971 -0.35451 -8.98864 1.59138 -3.50060 -11.71642 10.43516 8.18201
[461] -14.58988 4.91710 -7.22259 -2.63883 -4.84113 -0.38368 0.74348 1.57662 -6.37795 9.08419
[471] -4.88120 -9.80800 2.75498 19.11648 -6.75040 0.61883 5.02318 -0.43750 -9.71998 4.55130
[481] 4.65533 -5.95557 -1.64783 -8.95818 -11.83004 5.93306 5.08460 -16.00421 -2.47398 7.13274
[491] -8.62462 -4.26217 -10.50634 -6.34045 7.27456 -10.62809 5.30840 -4.74419 -4.76206 -3.74513
[501] 4.30048 8.37244 -0.61434 -6.82440 -1.33141 4.52402 13.14806 -7.23795 -11.04717 1.27587
[511] 7.85785 7.15648 -4.62579 8.79503 8.59337 0.13336 -6.58775 -3.35582 -3.78230 9.11007
[521] -4.32392 0.97297 1.95285 10.15643 4.41570 9.45688 -0.03124 -2.91259 -7.28229 -5.23713
[531] 5.09551 13.69087 -6.15977 4.55086 -3.77403 -0.79941 3.65810 8.36591 -2.02657 -2.08390
[541] 16.68501 0.66901 -7.79278 -4.69246 -14.12354 -6.02752 7.47830 -8.29074 -9.28136 8.18579
[551] 0.69957 5.71629 -3.24319 -4.03836 4.66481 11.02112 15.17714 -8.18491 14.60219 -8.37051
[561] -0.99550 -10.31264 -0.61431 -1.03096 -5.04767 2.48936 12.98982 -1.93032 -1.56462 -1.24008
[571] -9.08483 3.98283 -17.34103 -12.39267 -3.44971 -2.68159 1.51564 2.20357 -12.29948 9.80752
[581] -14.37618 2.80384 -5.00508 6.20246 -11.69552 -3.90801 -8.63978 4.46300 7.09745 -4.15487
[591] 2.86024 -3.20448 -8.56900 7.81890 -2.72164 0.85900 -15.02990 -1.17635 -1.28095 5.78900
[601] 1.37368 8.29555 -7.47849 2.42205 2.68630 -12.52266 -10.68540 5.87396 20.15916 -19.09497
[611] 3.40496 4.03689 6.96458 13.44747 -0.84731 1.83747 -7.55059 9.95183 -15.07585 0.16447
[621] 0.47122 3.57319 -0.57295 0.85751 6.31198 -7.64016 -1.20628 -10.47506 2.81206 -2.31528
[631] -2.18538 6.96322 3.85904 -3.08985 -0.78018 -0.44074 13.31577 -2.09426 2.37098 -6.95222
[641] 11.54578 -2.28373 3.58547 2.83131 -6.96746 1.35476 -2.82577 -5.30719 -5.15738 -3.53513
[651] -2.27071 -10.81718 2.52873 10.78710 0.58995 -3.77394 23.90205 16.96941 3.63778 -2.37676
[661] 4.14784 -1.45876 -0.51433 5.25121 -4.85009 1.68906 12.07853 9.17475 -13.22617 -5.58723
[671] 4.53172 -8.36025 -12.79123 -3.94993 -3.17967 2.13141 0.04840 -0.92287 7.18717 -2.97405
[681] -1.87123 5.59712 4.57467 -1.25091 -8.36981 -4.14314 -3.03748 -3.68798 -1.76825 5.05743
[691] 7.01455 10.62312 3.27583 7.43941 -7.90156 -2.49159 -6.62102 11.19564 15.33847 2.27724
[701] -0.65882 3.85682 3.41269 -4.56832 -6.15672 0.20299 10.66067 9.94390 -5.38442 -1.89620
[711] 2.93187 -19.09909 6.09052 -4.67877 21.36651 -0.97886 3.55853 0.19822 1.28536 -1.12138
[721] -3.15654 -9.62092 -6.49594 7.67597 3.20844 13.17165 -9.12641 7.07396 5.16062 5.17990
[731] -10.21281 -3.84346 18.85390 -16.67855 -11.72858 -2.01287 -14.50355 10.61636 -0.50898 -5.53342
[741] -3.16896 -5.39847 -0.30850 5.14996 8.68014 -11.91333 1.77128 6.94479 -15.46782 -7.30382
[751] 5.19234 -0.62978 -10.11059 13.35301 -9.94152 6.53848 5.10064 -2.19794 11.87526 -7.81431
[761] 4.22935 5.66007 -9.10613 2.27731 -2.38874 0.75511 5.04614 -5.88689 -4.47708 -6.93521
[771] 4.62171 -2.98139 -6.55029 1.35318 -2.26634 1.23321 -2.42234 -5.58933 7.74951 -2.52626
[781] -21.98034 -9.07733 6.65731 5.89711 -0.67201 14.94723 -1.72227 1.28336 -2.55218 6.27036
[791] -7.55694 -10.05438 2.33089 -6.47918 14.38109 7.97685 13.43863 5.72975 -6.27088 -5.12891
[801] -10.38483 -2.65587 -0.17726 1.93406 10.61523 12.77508 -3.30400 -1.52994 -8.36522 -2.50204
[811] -14.87248 1.15320 -15.47669 3.64903 -3.51109 3.38369 -7.70409 -9.33328 -9.56918 -11.96660
[821] -13.59116 3.75524 -15.06012 -3.21806 5.01985 2.79399 -6.09129 -3.89004 5.43588 -10.99740
[831] 0.62360 -2.50204 8.59965 -5.11086 16.02439 -15.36392 1.07489 -5.70692 -11.92035 -9.38351
[841] -15.00852 5.40399 -1.38646 -9.20040 -3.10081 -5.03051 -1.81434 -12.80361 -1.57690 -0.82307
[851] -11.53819 -4.31601 0.99441 4.08846 3.01525 -8.74020 12.03497 14.78479 -2.50670 3.19120
[861] -11.08006 1.42757 -8.86845 -8.02188 6.35772 1.23401 -17.15380 2.29389 8.58714 4.69361
[871] -1.30780 -2.87619 -9.44154 -9.48911 6.19711 -3.78805 3.14122 5.54141 -6.09216 7.33715
[881] -7.71926 4.34560 -3.47014 0.26588 -5.69945 -2.89963 -1.61871 -16.69608 -0.15318 -3.50287
[891] 10.97544 9.04545 7.95043 -2.78155 6.39848 -0.80594 -6.93367 -12.01238 -6.13132 -2.03949
[901] -6.47285 -15.51588 -1.01739 -4.42227 5.38898 3.56857 -4.56961 11.97815 -17.70165 -5.39138
[911] 18.09184 -8.50665 13.94961 0.69198 -2.35002 1.94383 0.34171 -7.21741 0.20248 -3.41240
[921] -0.92581 4.21961 5.49025 -15.20761 8.80558 -0.14488 -7.59720 -11.04855 -11.52112 1.39354
[931] 11.44962 -1.62357 -9.56391 5.19771 2.46518 14.23173 -1.50831 -1.03227 -3.22814 -1.73574
[941] -18.99407 -0.95427 -5.46847 -3.97505 -17.13381 3.04102 -1.84459 -4.29846 -8.07855 7.89818
[951] -11.65176 9.96206 1.10124 3.18137 1.55294 -9.91283 -2.05961 11.92111 -2.51286 7.59277
[961] -18.84041 -2.71859 12.91002 -1.14595 -9.13255 -2.63204 -7.55096 -0.76148 4.96732 4.45690
[971] -8.44975 -1.66066 -2.84893 -10.61449 -1.30864 4.70363 5.29430 -10.89069 9.64045 1.55495
[981] -8.34681 -8.32832 -0.16497 11.22528 0.58928 -4.96090 -7.47640 10.39034 -5.72071 -2.39909
[991] 0.03738 -7.35270 1.91139 -12.91750 7.07267 -15.27261 -8.61871 1.12946 -1.71334 -3.11270
[ reached getOption("max.print") -- omitted 7000 entries ]density_gramxgenxsynt <- density(pst_gramxgenxsynt)
plot(density_gramxgenxsynt, main = "Density Plot of pst_gramxgenxsynt", xlab = "pst_gramxgenxsynt values", ylab = "Density", col = "red")
# check posterior predicts --> the fits looks very good --> suspision of overfit? --> cv validation?
predicts <- y_posterior$Predict_rt
dim(predicts) # 8000 x 1271[1] 8000 1771y_true <- contr_et %>%
filter(AOI_id == region) %>% # Filter by the specified region
filter(!is.na(.data[[meas]])) %>% # Filter out NA values for the specific measure
pull(.data[[meas]])
ppc_dens_overlay(y_true, yrep = predicts[1:200, ])
compile model results
stats_df <- data.frame()
# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
for (meth in methods) {
# Loop over each measure type to read the corresponding model and extract data
for (region in regions) {
for (meas in measure_types) {
model_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
m1 <- readRDS(model_path)
# print(summary(m1))
# Extract posterior distributions
y_posterior <- extract(m1)
intercept <- exp(y_posterior$beta[,1])
betas <- c("b_0", "b_Gram", "b_Gen","b_Synt", "b_Lex",
"b_Gram_x_Synt", "b_Gram_x_Lex", "b_Gram_x_Gen_x_Synt", "b_Gram_x_Gen_x_Lex")
posterior_samples <- list(intercept, y_posterior$Gram, y_posterior$Gen, y_posterior$Synt, y_posterior$Lex,
y_posterior$Gram_x_Synt, y_posterior$Gram_x_Lex, y_posterior$Gram_x_Gen_x_Synt, y_posterior$Gram_x_Gen_x_Lex)
hpdi_95 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.95))
hpdi_89 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.89))
# Prepare the results data frame
temp_results <- data.frame(
method = rep(meth, length(betas)),
region = rep(region, length(betas)),
measure = rep(meas, length(betas)),
beta = betas,
bval_mean = sapply(posterior_samples, mean),
crI_95_lower = sapply(posterior_samples, function(x) quantile(x, 0.025)),
crI_95_upper = sapply(posterior_samples, function(x) quantile(x, 0.975)),
crl_89_lower = sapply(posterior_samples, function(x) quantile(x, 0.055)),
crl_89_upper = sapply(posterior_samples, function(x) quantile(x, 0.945)),
hpdi_95_lower = sapply(hpdi_95, function(x) x[1]),
hpdi_95_upper = sapply(hpdi_95, function(x) x[2]),
hpdi_89_lower = sapply(hpdi_89, function(x) x[1]),
hpdi_89_upper = sapply(hpdi_89, function(x) x[2]),
bval_median = sapply(posterior_samples, median)
)
# Append the temp_results to the stats_df data frame
stats_df <- rbind(stats_df, temp_results)
}
}
}
# View(stats_df)
stats_df <- stats_df %>%
mutate(across(
where(is.numeric),
~ if_else(measure %in% c("FPReg", "RegIn_incl"), round(., 3), round(., 0))
)) %>%
mutate(
annotation = paste0(round(bval_mean, 2),
" [", round(crI_95_lower, 2), ", ",
round(crI_95_upper, 2), "]")
)
write.csv(stats_df, "./stats2/stats_bayesian.csv", row.names = FALSE)regions <- c("R2", "R3", "R4", "R5")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
groups <- c("gender_match", "target_gender", "lex", "synt")
all_diffs <- data.frame()
# Loop through each region, measure type, and group
for (region in regions) {
for (meas in measure_types) {
for (group in groups) {
# Calculate mean and difference for each subgroup
summary_stats <- contr_motr %>%
mutate(lex = if_else(type=="stim_verb", "v", "a"),
synt = if_else(type=="stim_adj", "in", "ex")) %>%
filter(AOI_id == region) %>% # Filter by region
filter(!is.na(.data[[meas]])) %>% # Filter out NA values for measure
group_by(.data[[group]]) %>% # Group by current group variable
summarise(mean_value = mean(.data[[meas]], na.rm = TRUE)) %>% # Mean of measure
summarise(diff = diff(mean_value)) # Difference between the means
# Append the results to all_diffs
all_diffs <- rbind(all_diffs,
data.frame(
region = region,
measure_type = meas,
group = group,
diff = summary_stats$diff
))
}
}
}
all_diffsPLOT
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
# prepare motr for plotting
motr_plot <- contr_motr %>%
dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
) %>%
drop_na(total_duration) %>%
gather(measure, value, measure_types) %>%
filter(region %in%c(2, 3, 4, 5)) %>%
drop_na()
# View(motr_plot)
motr_lex <- motr_plot %>%
group_by(lex, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(lex, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(lex, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Lexical Category",
method = "MoTR") %>%
rename(type = lex)
motr_synt <- motr_plot %>%
group_by(synt, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(synt, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(synt, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Agreement Type",
method = "MoTR") %>%
rename(type = synt)
# plot et data for plotting
et_plot <- contr_et %>%
dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
) %>%
drop_na(total_duration) %>%
gather(measure, value, measure_types) %>%
filter(region %in%c(2, 3, 4, 5)) %>%
drop_na()
et_lex <- et_plot %>%
group_by(lex, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(lex, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(lex, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Lexical Category",
method = "Eye-tr.") %>%
rename(type = lex)
et_synt <- et_plot %>%
group_by(synt, gender_match, item_id, region, measure) %>%
summarise(
m = mean(value)
) %>%
ungroup() %>%
group_by(synt, region, measure) %>%
pivot_wider(
names_from = gender_match,
values_from = m,
names_prefix = "mean_"
) %>%
# Calculate the difference between 'Mis' and 'Match'
drop_na() %>%
mutate(
diff = mean_Mis - mean_Match
) %>%
group_by(synt, region, measure) %>%
summarise(
m_diff = mean(diff),
s = std.error(diff),
lower = m_diff - 1.96 * s,
upper = m_diff + 1.96 * s
) %>%
ungroup() %>%
mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
) %>%
mutate(Prediction = "Agreement Type",
method = "Eye-tr.") %>%
rename(type = synt)
motr_et_plot <- rbind(motr_lex, motr_synt, et_lex, et_synt)
stats_df <- read_csv("./stats/stats_bayesian.csv", show_col_types = FALSE)
annotation <- stats_df %>%
mutate(region = as.double(substr(region, 2, 2)),
measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob.")),
method = if_else(method=="motr", "MoTR", "Eye-tr."))%>%
filter(beta %in% c("b_Gram_x_Lex", "b_Gram_x_Synt")) %>%
mutate(Prediction = if_else(beta == "b_Gram_x_Lex", "Lexical Category", "Agreement Type")) %>%
dplyr::select(method, region, measure, Prediction, annotation)
View(annotation)
plot_annotated <- motr_et_plot %>%
left_join(annotation, by = c("method", "region", "measure", "Prediction")) %>%
mutate(annotation = if_else(is.na(annotation), "", annotation)) %>%
mutate(annotation = if_else(type %in% c("Verb", "External"), "", annotation))
plot_annotatedplot_annotated %>%
filter(method == "MoTR") %>%
filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point(aes(shape = type)) +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Reading time difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"External" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "MoTR",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = guide_legend(
title = "MoTR",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid")
)
)
)Warning: Duplicated `override.aes` is ignored.ggsave(paste0("./images/motr_rt_interaction.pdf"), device="pdf", height=6, width=8)Warning: Duplicated `override.aes` is ignored.
plot_annotated %>%
filter(method == "MoTR") %>%
filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point() +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Regression prob. difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"Agreement" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = "none"
)Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).ggsave(paste0("./images/motr_regression_interaction.pdf"), device="pdf", height=6, width=16/3)Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).
plot_annotated %>%
filter(method == "Eye-tr.") %>%
filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point(aes(shape = type)) +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Reading time difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"External" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid")
)
)
)Warning: Duplicated `override.aes` is ignored.ggsave(paste0("./images/et_rt_interaction.pdf"), device="pdf", height=6, width=8)Warning: Duplicated `override.aes` is ignored.
plot_annotated %>%
filter(method == "Eye-tr.") %>%
filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point() +
geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
geom_line() +
geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
facet_grid(Prediction ~ measure, scales = "free_y") +
labs(
# title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
y = "Regression prob. difference (Mis. - Match)",
x = "Sentence Region"
) +
scale_x_continuous(breaks = c(1:5)) +
scale_color_manual(values = c(
"Internal" = "#9467BD", # Purple
"External" = "#FF9DA7", # Orange
"Adjective" = "#F28E2B", # Pink (Contrasts with Green)
"Verb" = "#8C564B"
)) +
scale_shape_manual(values = c(
"Internal" = 16, # Filled circle
"Agreement" = 17, # Filled triangle
"Adjective" = 16, # Filled circle
"Verb" = 17 # Filled triangle
)) +
theme(
legend.position = "bottom",
plot.title = element_text(hjust = 0.5)
) +
guides(
linetype = "none",
color = guide_legend(
title = "Eye-tr.",
ncol = 4,
byrow = TRUE,
override.aes = list(
linetype = c("dotdash", "dotdash", "solid", "solid"),
shape = c(16, 17, 16, 17)
)
),
shape = "none"
)Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).ggsave(paste0("./images/et_regression_interaction.pdf"), device="pdf", height=6, width=16/3)Warning: Removed 8 rows containing missing values or values outside the scale range (`geom_point()`).
---
title: "Data Analysis for Russian MoTR Reading Data and ET data"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r libraries, echo=TRUE, results='hide', warning=FALSE, message=FALSE, eval=TRUE}
shhh <- suppressPackageStartupMessages # It's a library, so shhh!

shhh(library( mgcv ))
shhh(library(dplyr))
shhh(library(ggplot2))
shhh(library(lme4))
shhh(library(tidymv))
shhh(library(gamlss))
shhh(library(gsubfn))
shhh(library(lmerTest))
shhh(library(tidyverse))
shhh(library(boot))
shhh(library(rsample))
shhh(library(plotrix))
shhh(library(ggrepel))
shhh(library(mgcv))
shhh(library(brms))
shhh(library(bayesplot))
shhh(library(tidyr))
shhh(library(car))
shhh(library(HDInterval))
shhh(library(gridExtra))
shhh(library(posterior))
shhh(library(readxl))
shhh(library(stringr))
shhh(library(loo))
shhh(library(MASS))
shhh(library(hypr))
shhh(library(designr))
shhh(library(afex))

shhh(library(coda))
shhh(library(rstan))
shhh(library(rstantools))

rstan_options(auto_write=TRUE)
options(mc.cores=parallel::detectCores())
rstan_options(auto_write = TRUE)
theme_set(theme_bw())
options(digits=4)
options(scipen=999)
set.seed(444)

```

# Read in ET Data
```{r ET-Data, echo=TRUE, warning=FALSE, eval=TRUE}
file_list <- list.files("/Users/cui/Documents/uzh/PhD/Projects/Russian_Agreement/russian_gender/ref/Eyetracking/", pattern = "*.xlsx", full.names = TRUE)
et_raw <- file_list %>%
  lapply(read_excel) %>%
  bind_rows()
```

# clean ET raw data
```{r ET-clean, echo=TRUE, warning=FALSE, eval=TRUE}
select_meas <- c("SFD", "total_duration", "gaze_duration", "FPFix", "go_past_time", "FPReg", "RegIn")

et <- et_raw %>%
  dplyr::select(IA_LABEL, item, word.id, list, RECORDING_SESSION_LABEL, SFD, IA_DWELL_TIME, IA_FIRST_RUN_DWELL_TIME, IA_FIRST_FIX_PROGRESSIVE, IA_SELECTIVE_REGRESSION_PATH_DURATION, IA_REGRESSION_OUT, IA_REGRESSION_IN, gender_match, part, target_gender, type, Region, condition, ACCURACY, animacy) %>%
  rename(
    word = IA_LABEL,
    item_id = item,
    word_nr = word.id,
    subj_id = RECORDING_SESSION_LABEL,
    total_duration = IA_DWELL_TIME,
    gaze_duration = IA_FIRST_RUN_DWELL_TIME,
    first_pass_fix = IA_FIRST_FIX_PROGRESSIVE,
    go_past_time = IA_SELECTIVE_REGRESSION_PATH_DURATION,
    FPReg = IA_REGRESSION_OUT,
    RegIn = IA_REGRESSION_IN,
    AOI_id = Region
  ) %>%
  filter(subj_id != "russ34") %>%  # russ34 has acc 0.6 according to the calculation below.
  mutate(
    go_past_time = as.numeric(go_past_time),
    SFD = if_else(first_pass_fix == 1, SFD, 0),
    gaze_duration = if_else(first_pass_fix == 1, gaze_duration, 0),
    go_past_time = if_else(first_pass_fix == 1,  go_past_time, 0),
  ) %>%
  rename(FPFix = first_pass_fix) %>%
  mutate(
         FPReg = ifelse(gaze_duration==0, NA, FPReg),
         FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
  gather(measure, value, select_meas) %>%
  mutate(
    value = as.numeric(value),
    tgt_zero = if_else(measure %in% c("SFD", "gaze_duration", "go_past_time", "total_duration") & value == 0, F, T)) %>%
  filter(tgt_zero != F) %>%
  dplyr::select(-tgt_zero, -condition) %>%
  mutate(item_id = as.factor(item_id),
         subj_id = as.factor(subj_id)) %>%
  spread(measure, value) %>%

  # Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
  # We filter outliers only for aesthetic reasons in plotting.

  gather(measure, value, c("SFD", "gaze_duration", "go_past_time", "total_duration")) %>%
  mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
  filter(outlier == FALSE) %>%
  dplyr::select(-outlier) %>%
  spread(measure, value) %>%

  gather(measure, value, select_meas) %>%
  mutate(cond = case_when(
    target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
    target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
     target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
    target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
    target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
    target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
    target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
    target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
    target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
    target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
    target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
    target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
    TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
  )) %>% 
  # filter(animacy %in% c("Inanim", "inanim")) %>% 
  dplyr::select(-list, -part, -animacy) 

et 
```


# Read in MoTR Data
```{r MoTR-Data, echo=TRUE, warning=FALSE, eval=TRUE}
# The path to the data
data_path <- "./data/"
data_names <- list.files(data_path)

# Read in the data from each participant and add to the data frame
motr_df <- data.frame()
for(name in data_names){
  subj <- gsub("reader_", "", gsub("_reading_measures.csv", "", name))
  temp_df <- read.csv(paste0(data_path, "/", name)) %>% mutate(subj_id = subj)
  motr_df <- rbind(motr_df, temp_df)
} 

motr_df <- motr_df %>% mutate(word_len = nchar(word),
                              word_length = scale(word_len)[,1]) %>% 
  group_by(subj_id, item_id) %>%
  arrange(subj_id, item_id) %>%
  mutate(word_len_pre1 = lag(word_length, n = 1),
         word_len_pre2 = lag(word_length, n = 2)) %>%
  ungroup()

# Clean the data
motr <- motr_df %>%
  # filter(subj_id != 171) %>%   # acc = 0.8
  filter(! list %in% c(98, 99)) %>% # filter practice and filler items
  mutate(skip = ifelse(total_duration==0, 1, 0),
         FPReg = ifelse(gaze_duration==0, NA, FPReg),
         FPFix = ifelse(gaze_duration==0, NA, FPFix)) %>%
  filter(skip == 0) %>%
  gather(measure, value, 18:26) %>%
  mutate(tgt_zero = if_else(measure %in% c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration") & value == 0, F, T)) %>%
  filter(tgt_zero != F) %>%
  dplyr::select(-tgt_zero, -cond_id, -skip, -word_len) %>%
  mutate(item_id = as.factor(item_id),
         subj_id = as.factor(subj_id)) %>%
  spread(measure, value) %>%
  
  # Note: we commented these lines out when running models because we logged the data and used mix effects to account for the variances and noises. If also filter outliers when running models, the results will not change qualitatively, but the estimated CI (or CrI) will be a bit narrower.
  # We filter outliers only for aesthetic reasons in plotting.
  
  gather(measure, value, c("first_duration", "gaze_duration", "go_past_time", "right_bounded_rt", "total_duration")) %>%
  mutate(outlier = value > (mean(value, na.rm = TRUE) + 3 * sd(value, na.rm = TRUE))) %>%
  filter(outlier == FALSE) %>%
  dplyr::select(-outlier) %>%
  spread(measure, value) %>%
  
  gather(measure, value, 21:29) %>%
  mutate(cond = case_when(
    target_gender == "M" & gender_match == "Mis" & type == "stim_adj" ~ "a",
    target_gender == "M" & gender_match == "Mis" & type == "stim_verb" ~ "b",
     target_gender == "M" & gender_match == "Mis" & type == "stim_pred_adj" ~ "c",
    target_gender == "M" & gender_match == "Match" & type == "stim_adj" ~ "d",
    target_gender == "M" & gender_match == "Match" & type == "stim_verb" ~ "e",
    target_gender == "M" & gender_match == "Match" & type == "stim_pred_adj" ~ "f",
    target_gender == "F" & gender_match == "Mis" & type == "stim_adj" ~ "g",
    target_gender == "F" & gender_match == "Mis" & type == "stim_verb" ~ "h",
    target_gender == "F" & gender_match == "Mis" & type == "stim_pred_adj" ~ "i",
    target_gender == "F" & gender_match == "Match" & type == "stim_adj" ~ "j",
    target_gender == "F" & gender_match == "Match" & type == "stim_verb" ~ "k",
    target_gender == "F" & gender_match == "Match" & type == "stim_pred_adj" ~ "l",
    TRUE ~ NA_character_ # This is the default case if none of the above conditions are met
  )) %>%
  dplyr::select(-list, -part, -type_id, -orig_item_number, -case, -animacy, -response_true, -response_chosen) %>%
  mutate(word = str_replace_all(word, "\\.", "")) %>%
  rowwise() %>%
  mutate(log_freq = ifelse(word %in% et_raw$IA_LABEL, 
                           et_raw$lg_frequency[match(word, et_raw$IA_LABEL)], 
                           NA_real_)) %>%
  ungroup()

# View(motr)

```

# ACC by participant
```{r CORRECTNESS SUBJ, eval=FALSE}
motr_acc <- motr %>% dplyr::select(item_id, cond, subj_id, correctness) %>%
  filter(correctness != 99) %>%
  distinct()

motr_acc_summary <- motr_acc %>%
  group_by(subj_id) %>%
  summarise(mean_correctness = mean(correctness),
            sd_correctness = sd(correctness),
            count = n())

# only subj_id 171 get acc = 0.8; others all > 0.88 (incl. fillers)

# write.csv(motr_acc_summary, "./stats/correctness_summary.csv", row.names = FALSE)

et_acc <- et %>% dplyr::select(all_of(c("item_id", "subj_id", "cond", "ACCURACY"))) %>%
  # filter(ACCURACY != -1) %>%
  distinct() %>%
  mutate(correctness = as.numeric(unlist(ACCURACY)))

et_acc_summary <- et_acc %>%
  group_by(subj_id) %>%
  summarise(mean_correctness = mean(correctness),
            sd_correctness = sd(correctness),
            count = n())

# only subj_id russ34 get acc = 0.6; others all > 0.8 (excl. fillers)

```

# ACC by item
```{r CORRECTNESS COND, eval=FALSE}
motr_acc_cond <- motr_acc %>%
  group_by(cond) %>%
  summarise(
    mean_correctness = round(mean(correctness), 2),
    sd_correctness = round(sd(correctness), 2),
    count = n()
  )
motr_acc_cond

et_acc_cond <- et_acc %>%
  group_by(cond) %>%
  summarise(
    mean_correctness = round(mean(correctness), 2),
    sd_correctness = round(sd(correctness), 2),
    count = n()
  )

et_acc_cond
```

### RESEARCH QUESTIONS:
 1. Are RTs significantly different between gender-match and gender-mismatch conditions?
 ==> main effect of grammaticality (gender match or not)
 
 2. Are RTs different in Masculine versus Feminine sentence conditions?
 ==> main effect of gender of target word
 
 3. Are RTs affected by lexical category (whether different lexical categories of the agreeing element will make the processing more difficult or not)? --> ADJ(adj & pre_adj) v.s. VERB 
 ==> main effect of lexical type of sentences. 
 
 4. Are RTs affected by feature matching mechanism(whether agreeing element instantiates internal v.s. external agreement will make a difference in processing difficulty)? --> External (verb & predicative adjective) v.s. Internal (modifying adjective) 
 ==> main effect of syntax type of sentences.
 
 5. Whether the effect of grammaticality is modulated by lexical category? --> Is the RT difference caused by grammaticality effect affected by the target word being Adj or Verb? ==> interaction between grammaticality and lexical type
 
 6. Whether the effect of grammaticality depends on the feature matching mechanism of the sentence? --> Is the RT difference caused by grammaticality effect affected by the mechanism being external or internal? ==> interaction between grammaticality and feature matching mechanism
 
 7. Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between lexical category (Adj v.s. Verb)? ==> 3-way interaction between grammaticality, gender and lexical category
 
 8. Does the (possible) difference in the sensitivity to the grammaticality manipulation of Masculine versus Feminine conditions differ between feature matching mechanism (External v.s. Internal)? ==> 3-way interaction between grammaticality, gender and feature matching mechanism

# contrast coding
```{r factorize, echo=TRUE, eval=TRUE}
# check conditions
et$cond <- factor(et$cond)
levels(et$cond)
motr$cond <- factor(motr$cond)
levels(motr$cond)
```

# Create hypothesis matrix from RQ
```{r}
## sol1 
X_H <- matrix(c(1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12,1/12, # Intercept
                1/6,1/6,1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6, # Main effect of grammaticality
                1/6,1/6,1/6,1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6, # Main effect of gender
                1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8, # Main effect of feature matching
                -1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8,-1/8,1/4,-1/8, # Main effect of lexical category
                1/6,1/6,1/6,-1/6,-1/6,-1/6,-1/6,-1/6,-1/6,1/6,1/6,1/6, # gram x gen
                1/4,-1/8,-1/8,-1/4,1/8,1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8, # gram x synt
                -1/8,1/4,-1/8,1/8,-1/4,1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8, # gram x lex
                1/4,-1/8,-1/8,1/4,-1/8,-1/8,-1/4,1/8,1/8,-1/4,1/8,1/8,  #gen x synt
                -1/8,1/4,-1/8,-1/8,1/4,-1/8,1/8,-1/4,1/8,1/8,-1/4,1/8, # gen x lex
                1/2,-1/4,-1/4,-1/2,1/4,1/4,-1/2,1/4,1/4,1/2,-1/4,-1/4,  # gram x gen x synt
                -1/4,1/2,-1/4,1/4,-1/2,1/4,1/4,-1/2,1/4,-1/4,1/2,-1/4 # gram x gen x lex
                
), byrow=TRUE, nrow = 12)
# X_H
# rowSums(X_H) # ensure centering

X_C = ginv(X_H)
rownames(X_C) <- c('a','b','c','d','e','f','g','h', 'i', 'j', 'k', 'l')
colnames(X_C) <- c('Int','Gram','Gen','Lex','Synt','Gram_x_Gen','Gram_x_Lex','Gram_x_Synt','Gen_x_Lex','Gen_x_synt','Gram_x_Gen_Lex','Gram_x_Gen_Synt')
X_C_bar <- X_C[,2:ncol(X_C)]
fractions(X_C_bar)
```

```{r Custom contrasts, echo=TRUE, eval=TRUE}
contr_motr <- motr %>% 
  mutate(
    #--------------------- main effects ---------------------
    Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality 
    Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
    Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching  (a vs pv)
    Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (ap vs v)
    
    #--------------------- 2-way interactions ---------------------
    Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
    Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
    Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
    Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
    Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
    
    #--------------------- 3 way interection ---------------------
    Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
    Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
  ) %>% spread(measure, value) #%>%
  # # filter(word_nr == 3)
  # filter(AOI_id == "R3")
  
contr_motr

contr_et <- et %>% 
  mutate(
    #--------------------- main effects ---------------------
    Gram = ifelse(cond %in% c('a', 'b', 'c', 'g', 'h', 'i'), 1/2, -1/2), # Main effect grammaticality 
    Gen = ifelse(cond %in% c('a','b','c','d','e', 'f'), 1/2, -1/2), # Main effect gender
    Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of feature matching  (a vs pv)
    Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'i', 'l'), -2/3, 2/3)), # Main effect of lexical category (v vs ap)
    
    #--------------------- 2-way interactions ---------------------
    Gram_x_Gen = ifelse(cond %in% c('a', 'b', 'c', 'j', 'k', 'l'), 1/2, -1/2), # Grammaticality x Gender
    Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'f', 'g', 'j'), -2/3, 2/3)), # Gender x Feature matching
    Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'f', 'h', 'k'), -2/3, 2/3)), # Gender x Lexical category
    Gram_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'i', 'j'), -2/3, 2/3)), # Grammaticality x Feature matching
    Gram_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'i', 'k'), -2/3, 2/3)), # Grammaticality x Lexical Category
    
    #--------------------- 3 way interection ---------------------
    Gram_x_Gen_x_Synt = ifelse(cond %in% c('b', 'e', 'h', 'k'), 0,
                        ifelse(cond %in% c('c', 'd', 'g', 'l'), -1/3, 1/3)), # gen x synt(ap v) x gram
    Gram_x_Gen_x_Lex = ifelse(cond %in% c('a', 'd', 'g', 'j'), 0,
                        ifelse(cond %in% c('c', 'e', 'h', 'l'), -1/3, 1/3)) # gen x lex(ap v) x gram
  ) %>% spread(measure, value) %>%
  rename(RegIn_incl = RegIn)
# View(contr_et)
```

```{r Store data for Power Analysis, echo=TRUE, eval=False}
# write.csv(contr_et, "./stats/et_reading_measures_contrast_coded.csv", row.names = FALSE)
# write.csv(contr_motr, "./stats/motr_reading_measures_contrast_coded.csv", row.names = FALSE)
```


```{r Constrasts sanity check, echo=TRUE, eval=False}
## sol2 --> try hypr package, also for sanity check
hypothesis_matrix <- hypr(
  Gram = (a+b+c+g+h+i)/6 ~ (d+e+f+j+k+l)/6,
  Gen = (a+b+c+d+e+f)/6 ~ (g+h+i+j+k+l)/6,
  Synt = (a+d+g+j)/4 ~ (b+c+e+f+h+i+k+l)/8,
  Lex = (b+e+h+k)/4 ~ (a+c+d+f+g+i+j+l)/8,
  # Gram_x_Gen = ((a+b+c)/3-(d+e+f)/3)/2 ~ ((g+h+i)/3-(j+k+l)/3)/2,
  Gram_x_Synt = ((e+f+k+l)/4-(d+j)/2)/2 ~ ((b+c+h+i)/4-(a+g)/2)/2 ,
  Gram_x_Lex =  ((d+f+j+l)/4-(e+k)/2)/2 ~ ((a+c+g+i)/4-(b+h)/2)/2,
  # Gen_x_Synt = ((h+i+k+l)/4-(g+j)/2)/2 ~ ((b+c+e+f)/4-(a+d)/2)/2,
  # Gen_x_Lex = ((g+i+j+l)/4-(h+k)/2)/2 ~ ((a+c+d+f)/4-(b+e)/2)/2,
  Gram_x_Gen_x_Synt = (((h+i)/2-g)-((k+l)/2-j))/2 ~ (((b+c)/2-a)-((e+f)/2-d))/2,
  Gram_x_Gen_x_Lex = (((g+i)/2-h)-((j+l)/2-k))/2 ~ (((a+c)/2-b)-((d+f)/2-e))/2
)

# Display the matrix
hypothesis_matrix
```


```{r freq-modeling, echo=TRUE, eval=FALSE, message=TRUE}

stats_freq = data.frame()
# regions = c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions = c("R3")
measure_types = c("gaze_duration", "go_past_time", "total_duration",
"FPReg", "RegIn_incl"
)

for (meth in methods) {
  for (region in regions) {
    for (meas in measure_types){
      print(paste("Fitting model for:", meas, "in Region:", region))
      if (meas %in% c("first_duration", "gaze_duration", "go_past_time", "total_duration")){
        if (meth == "motr") {
          model <- contr_motr %>% 
            filter(AOI_id == region) %>% 
            filter(!is.na(.data[[meas]]))  %>% 
            lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
                (1 | item_id) + (1 + Gram | subj_id)")),
                data = ., REML = F)
        } else {
          model <- contr_et %>% 
            filter(AOI_id == region) %>% 
            filter(!is.na(.data[[meas]]))  %>% 
            lmer(as.formula(paste("log(", meas, ") ~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt +
                (1 | item_id) + (1 + Gram | subj_id)")),
                data = ., REML = F)
        }
      coefs <- summary(model)$coefficients
      temp_results <- data.frame(
        method = meth,
        region = region,
        measure = meas,
        beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt", 
                 "b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
        bval = coefs[, "Estimate"],
        pval = coefs[, "Pr(>|t|)"]
          )
      }else{
        if (meth == "motr") {
          model <- contr_motr %>% filter(!is.na(.data[[meas]]))  %>% 
            glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt + 
                (1 | item_id) + (1 | subj_id)")), 
                data = ., family=binomial(link = "logit"))
        } else{
          model <- contr_et %>% filter(!is.na(.data[[meas]]))  %>% 
            glmer(as.formula(paste(meas, "~ Gram + Gen + Lex + Synt + Gram_x_Lex + Gram_x_Synt + Gram_x_Gen_x_Lex + Gram_x_Gen_x_Synt + 
                (1 | item_id) + (1 | subj_id)")), 
                data = ., family=binomial(link = "logit"))
        }
      coefs <- summary(model)$coefficients
      temp_results <- data.frame(
        method = meth,
        region = region,
        measure = meas,
        beta = c("b_0", "b_Gram", "b_Gen", "b_Lex", "b_Synt", 
                 "b_Gram_x_Lex", "b_Gram_x_Synt", "b_Gram_x_Gen_x_Lex", "b_Gram_x_Gen_x_Synt"),
        bval = coefs[, "Estimate"],
        pval = coefs[, "Pr(>|z|)"]
        )
      }
    stats_freq = rbind(stats_freq, temp_results)
  }
}
}
stats_freq <- stats_freq %>%
  mutate(sig = case_when(
    pval < 0.001 ~ "***",
    pval < 0.01  ~ "**",
    pval < 0.05  ~ "*",
    pval < 0.1   ~ ".",
    TRUE         ~ ""
  ))

# View(stats_freq)

# write.csv(stats_freq, "./stats/stats_motr_et_lmer.csv", row.names = FALSE)

```


# Fit Bayesian models
# function for creating stan data format
```{r createStanData, echo=TRUE, eval=FALSE}
createStanData <-function(d, dv,form){
  
  subj <- as.integer(factor(d$subj_id))
  N_subj <- length(unique(subj))
  item <- as.integer(factor(d$item_id))
  N_items <- length(unique(item))
  X <- unname(model.matrix(form, d))  
  attr(X, which="assign") <- NULL
  
  stanData <- list(N = nrow(X),           
                  P = ncol(X),              
                  n_u = ncol(X),             
                  n_w = 3,            
                  X = X,                     
                  Z_u = X,                 
                  Z_w = X[, 1:3, drop = FALSE],    # only by-item random intercept, Gram, Gen              
                  J = N_subj,                
                  K = N_items,
                  dv = dv,                    
                  subj = subj,
                  item = item)
  stanData
}
```

```{r bayesian-modeling, echo=TRUE, eval=FALSE, message=TRUE}
# note: this chunk takes time to run ~ 1 hour for one region

# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
# measure_types <- c("go_past_time")

for (meth in methods) {
  for (region in regions) {
    for (meas in measure_types) {
      print(paste("Fitting Bayesian model for:", meas, "in Region:", region))
      if (meth == "motr"){
        # Filter data for current region and non-missing measure
        temp <- contr_motr %>%
          filter(AOI_id == region) %>%
          filter(!is.na(.data[[meas]]))
      } else {
        temp <- contr_et %>%
          filter(AOI_id == region) %>%
          filter(!is.na(.data[[meas]]))
      }
      # binary dv
      if (meas %in% c("FPReg", "RegIn_incl")) {
        stan_data <- createStanData(
          d = temp,
          form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"), 
          dv = temp[[meas]]
        )
       stan_model_file <- "stan/Model_binary.stan"
      } else {
        # For other measures, use the default formulas and models
        stan_data <- createStanData(
          d = temp,
          form = as.formula("~1 + Gram + Gen + Synt + Lex + Gram_x_Synt + Gram_x_Lex + Gram_x_Gen_x_Synt + Gram_x_Gen_x_Lex"), 
          dv = temp[[meas]]
        )
        stan_model_file <- "stan/Model_RT.stan"
      }
      # Fit model 
      stan_model <- stan(
        file = stan_model_file, 
        data = stan_data,
        iter = 4000, 
        chains = 4,
        control = list(adapt_delta = 0.99)
      )
      # Save model output
      model_save_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
      saveRDS(stan_model, file = model_save_path)
    }
  }
}

```


# Examine fitted stan models
```{r Model_examination, echo=TRUE, eval=False}
# change xx.rds to other models to check them
region <- "R3"
meas <- "go_past_time"

model_path <- paste0("models/et_", meas, "_", region, ".rds")
m1_gd <- readRDS(model_path)

summary(m1_gd)
# check params
summary(m1_gd, pars = c('beta[1]', 'beta[2]', 'beta[3]', 'beta[4]', 'beta[5]', 'beta[6]', 'beta[7]', 'beta[8]', 'beta[9]'))

# check convergence
traceplot(m1_gd, pars = c("beta"))

# check predicts --> posterior parameter distr.
y_posterior <- extract(m1_gd) 
y_posterior$beta[,1] #intercept
y_posterior$beta[,2] #Gram
y_posterior$beta[,3] #Gen
y_posterior$beta[,4] #Synt
y_posterior$beta[,5] #Lex
y_posterior$beta[,6] #Gram_x_Synt
y_posterior$beta[,7] #Gram_x_Lex

# check predicts --> posterior parameter distr. back in normal space
pst_gram <- y_posterior$Gram
pst_gram
density_gram <- density(pst_gram)
plot(density_gram, main = "Density Plot of pst_gram", xlab = "pst_gram values", ylab = "Density", col = "red")

pst_gen <- y_posterior$Gen
pst_gen
density_gen <- density(pst_gen)
plot(density_gen, main = "Density Plot of pst_gen", xlab = "pst_gen values", ylab = "Density", col = "red")

pst_lex <- y_posterior$Lex
pst_lex
density_lex <- density(pst_lex)
plot(density_lex, main = "Density Plot of pst_lex", xlab = "pst_lex values", ylab = "Density", col = "red")

pst_synt <- y_posterior$Synt
pst_synt
density_synt <- density(pst_synt)
plot(density_synt, main = "Density Plot of pst_synt", xlab = "pst_synt values", ylab = "Density", col = "red")

pst_gramxlex <- y_posterior$Gram_x_Lex
pst_gramxlex
density_gramxlex <- density(pst_gramxlex)
plot(density_gramxlex, main = "Density Plot of pst_gramxlex", xlab = "pst_gramxlex values", ylab = "Density", col = "red")

pst_gramxsynt <- y_posterior$Gram_x_Synt
pst_gramxsynt
density_gramxsynt <- density(pst_gramxsynt)
plot(density_gramxsynt, main = "Density Plot of pst_gramxsynt", xlab = "pst_gramxtyps values", ylab = "Density", col = "red")

pst_gramxgenxlex <- y_posterior$Gram_x_Gen_x_Lex
pst_gramxgenxlex
density_gramxgenxlex <- density(pst_gramxgenxlex)
plot(density_gramxgenxlex, main = "Density Plot of pst_gramxgenxlex", xlab = "pst_gramxgenxlex values", ylab = "Density", col = "red")


pst_gramxgenxsynt <- y_posterior$Gram_x_Gen_x_Synt
pst_gramxgenxsynt
density_gramxgenxsynt <- density(pst_gramxgenxsynt)
plot(density_gramxgenxsynt, main = "Density Plot of pst_gramxgenxsynt", xlab = "pst_gramxgenxsynt values", ylab = "Density", col = "red")


# check posterior predicts --> the fits looks very good --> suspision of overfit? --> cv validation?
predicts <- y_posterior$Predict_rt
dim(predicts) # 8000 x 1271
y_true <- contr_et %>%
  filter(AOI_id == region) %>%             # Filter by the specified region
  filter(!is.na(.data[[meas]])) %>%        # Filter out NA values for the specific measure
  pull(.data[[meas]]) 

ppc_dens_overlay(y_true, yrep = predicts[1:200, ])

```

# compile model results
```{r compile_restuls, echo=TRUE, eval=TRUE}
stats_df <- data.frame()

# regions <- c("R2", "R3", "R4", "R5")
methods = c("motr", "et")
regions <- c("R3")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")

for (meth in methods) {
  # Loop over each measure type to read the corresponding model and extract data
  for (region in regions) {
    for (meas in measure_types) {
      model_path <- paste0("models/", meth, "_", meas, "_", region, ".rds")
      m1 <- readRDS(model_path)
      # print(summary(m1))
      # Extract posterior distributions
      y_posterior <- extract(m1)
      intercept <- exp(y_posterior$beta[,1])
    
      betas <- c("b_0", "b_Gram", "b_Gen","b_Synt", "b_Lex",
                 "b_Gram_x_Synt", "b_Gram_x_Lex", "b_Gram_x_Gen_x_Synt", "b_Gram_x_Gen_x_Lex")
      posterior_samples <- list(intercept, y_posterior$Gram, y_posterior$Gen, y_posterior$Synt, y_posterior$Lex,
                                y_posterior$Gram_x_Synt, y_posterior$Gram_x_Lex, y_posterior$Gram_x_Gen_x_Synt, y_posterior$Gram_x_Gen_x_Lex)
      
      hpdi_95 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.95))
      hpdi_89 <- lapply(posterior_samples, function(x) hdi(x, credMass = 0.89))
    
      # Prepare the results data frame
      temp_results <- data.frame(
        method = rep(meth, length(betas)),
        region = rep(region, length(betas)),
        measure = rep(meas, length(betas)),
        beta = betas,
        bval_mean = sapply(posterior_samples, mean),
        crI_95_lower = sapply(posterior_samples, function(x) quantile(x, 0.025)),
        crI_95_upper = sapply(posterior_samples, function(x) quantile(x, 0.975)),
        crl_89_lower = sapply(posterior_samples, function(x) quantile(x, 0.055)),
        crl_89_upper = sapply(posterior_samples, function(x) quantile(x, 0.945)),
        hpdi_95_lower = sapply(hpdi_95, function(x) x[1]),
        hpdi_95_upper = sapply(hpdi_95, function(x) x[2]),
        hpdi_89_lower = sapply(hpdi_89, function(x) x[1]),
        hpdi_89_upper = sapply(hpdi_89, function(x) x[2]),
        bval_median = sapply(posterior_samples, median)
      )
    
      # Append the temp_results to the stats_df data frame
      stats_df <- rbind(stats_df, temp_results)
    }
  }
  
}

# View(stats_df)

stats_df <- stats_df %>%
  mutate(across(
    where(is.numeric),
    ~ if_else(measure %in% c("FPReg", "RegIn_incl"), round(., 3), round(., 0))
  )) %>% 
  mutate(
    annotation = paste0(round(bval_mean, 2), 
                    " [", round(crI_95_lower, 2), ", ", 
                    round(crI_95_upper, 2), "]")
  )
write.csv(stats_df, "./stats/stats_bayesian.csv", row.names = FALSE)
```


```{r Model Sanity check, eval=FALSE}
regions <- c("R2", "R3", "R4", "R5")
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")
groups <- c("gender_match", "target_gender", "lex", "synt")

all_diffs <- data.frame()

# Loop through each region, measure type, and group
for (region in regions) {
  for (meas in measure_types) {
    for (group in groups) {
      
      # Calculate mean and difference for each subgroup
      summary_stats <- contr_motr %>%
        mutate(lex = if_else(type=="stim_verb", "v", "a"),
               synt = if_else(type=="stim_adj", "in", "ex")) %>%
        filter(AOI_id == region) %>%            # Filter by region
        filter(!is.na(.data[[meas]])) %>%       # Filter out NA values for measure
        group_by(.data[[group]]) %>%            # Group by current group variable
        summarise(mean_value = mean(.data[[meas]], na.rm = TRUE)) %>%  # Mean of measure
        summarise(diff = diff(mean_value))      # Difference between the means
      
      # Append the results to all_diffs
      all_diffs <- rbind(all_diffs, 
                         data.frame(
                           region = region, 
                           measure_type = meas, 
                           group = group, 
                           diff = summary_stats$diff
                         ))
    }
  }
}

all_diffs
```

# PLOT

```{r PREP PLOT, eval=TRUE}
measure_types <- c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl")

# prepare motr for plotting
motr_plot <- contr_motr %>%
  dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
  mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
  mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
         lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
         ) %>%
  drop_na(total_duration) %>%
  gather(measure, value, measure_types) %>%
  filter(region %in%c(2, 3, 4, 5)) %>%
  drop_na()

# View(motr_plot)

motr_lex <- motr_plot %>%
  group_by(lex, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(lex, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(lex, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>%
  ungroup() %>%
  mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Lexical Category",
          method = "MoTR") %>%
  rename(type = lex) 

motr_synt <- motr_plot %>%
  group_by(synt, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(synt, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(synt, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>% 
  ungroup() %>%
  mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Agreement Type",
         method = "MoTR") %>%
  rename(type = synt)

# plot et data for plotting 
et_plot <- contr_et %>%
  dplyr::select(item_id, type, target_gender, gender_match, word_nr, word, AOI_id, subj_id, cond, gaze_duration, go_past_time, total_duration, FPReg, RegIn_incl) %>%
  mutate(region = as.double(substr(AOI_id, 2, 2))) %>%
  mutate(synt = ifelse(type %in% c('stim_adj'), "Internal", "External"),
         lex = ifelse(type %in% c('stim_verb'), "Verb", "Adjective")
         ) %>%
  drop_na(total_duration) %>%
  gather(measure, value, measure_types) %>%
  filter(region %in%c(2, 3, 4, 5)) %>%
  drop_na()

et_lex <- et_plot %>%
  group_by(lex, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(lex, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(lex, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>%
  ungroup() %>%
  mutate(lex = factor(lex, levels=c("Adjective", "Verb"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Lexical Category",
         method = "Eye-tr.") %>%
  rename(type = lex)

et_synt <- et_plot %>%
  group_by(synt, gender_match, item_id, region, measure) %>%
    summarise(
      m = mean(value)
    ) %>%
  ungroup() %>%
  group_by(synt, region, measure) %>%
  pivot_wider(
    names_from = gender_match,
    values_from = m,
    names_prefix = "mean_"
  ) %>%
  # Calculate the difference between 'Mis' and 'Match'
  drop_na() %>%
  mutate(
    diff = mean_Mis - mean_Match
  ) %>%
  group_by(synt, region, measure) %>%
  summarise(
    m_diff = mean(diff),
    s = std.error(diff),
    lower = m_diff - 1.96 * s,
    upper = m_diff + 1.96 * s
  ) %>% 
  ungroup() %>%
  mutate(synt = factor(synt, levels=c("Internal", "External"))) %>%
  mutate(measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob."))
         ) %>%
  mutate(Prediction = "Agreement Type",
         method = "Eye-tr.") %>%
  rename(type = synt)

motr_et_plot <- rbind(motr_lex, motr_synt, et_lex, et_synt)

stats_df <- read_csv("./stats/stats_bayesian.csv", show_col_types = FALSE)

annotation <- stats_df %>% 
  mutate(region = as.double(substr(region, 2, 2)),
         measure = factor(measure, levels = c("gaze_duration", "go_past_time", "total_duration", "FPReg", "RegIn_incl"), labels=c("Gaze Duration", "Go Past Time", "Total Duration", "First Pass Regression out Prob.", "Regression in Prob.")),
          method = if_else(method=="motr", "MoTR", "Eye-tr."))%>% 
  filter(beta %in% c("b_Gram_x_Lex", "b_Gram_x_Synt")) %>%
  mutate(Prediction = if_else(beta == "b_Gram_x_Lex", "Lexical Category", "Agreement Type")) %>%
  dplyr::select(method, region, measure, Prediction, annotation)
View(annotation)

```

```{r}
plot_annotated <- motr_et_plot %>%
  left_join(annotation, by = c("method", "region", "measure", "Prediction")) %>%
  mutate(annotation = if_else(is.na(annotation), "", annotation)) %>%
  mutate(annotation = if_else(type %in% c("Verb", "External"), "", annotation))

plot_annotated
```


```{r}
plot_annotated %>%
  filter(method == "MoTR") %>%
  filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point(aes(shape = type)) +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Reading time difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "External" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "MoTR",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = guide_legend(
      title = "MoTR",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid")
      )
    )
  )
ggsave(paste0("./images/motr_rt_interaction.pdf"), device="pdf", height=6, width=8)
```

```{r}
plot_annotated %>%
  filter(method == "MoTR") %>%
  filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point() +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Regression prob. difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "Agreement" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = "none"
  )
ggsave(paste0("./images/motr_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
```

```{r}
plot_annotated %>%
  filter(method == "Eye-tr.") %>%
  filter(measure %in% c("Gaze Duration", "Go Past Time", "Total Duration")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction)) +
  geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = lower - 100, ymax = upper + 100), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point(aes(shape = type)) +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 50), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Reading time difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "External" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid")
      )
    )
  )
ggsave(paste0("./images/et_rt_interaction.pdf"), device="pdf", height=6, width=8)
```

```{r}
plot_annotated %>%
  filter(method == "Eye-tr.") %>%
  filter(measure %in% c("First Pass Regression out Prob.", "Regression in Prob.")) %>%
  ggplot(aes(x = region, y = m_diff, color = type, group = interaction(Prediction, type), linetype = Prediction, shape = type)) +
    geom_rect(aes(xmin = 2.5, xmax = 3.5, ymin = 0, ymax = upper + 0.2), color = NA, fill = "green", alpha = 0.01) +
    geom_hline(yintercept = 0, color = "gray30") + 
    geom_point() +
    geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.2) +
    geom_line() +
    geom_text(aes(label = annotation, y = upper + 0.1), vjust = 0, color = "black", size=3) +
    facet_grid(Prediction ~ measure, scales = "free_y") +
    labs(
      # title = "Interaction between Grammaticality and \n Feature-match Mechanism / Lexical Category",
      y = "Regression prob. difference (Mis. - Match)",
      x = "Sentence Region"
    ) +
  scale_x_continuous(breaks = c(1:5)) +
  scale_color_manual(values = c(
    "Internal" = "#9467BD",  # Purple
    "External" = "#FF9DA7",  # Orange
    "Adjective" = "#F28E2B",  # Pink (Contrasts with Green)
    "Verb" = "#8C564B" 
  )) +
  scale_shape_manual(values = c(
    "Internal" = 16, # Filled circle
    "Agreement" = 17, # Filled triangle
    "Adjective" = 16, # Filled circle
    "Verb" = 17 # Filled triangle
  )) +
  theme(
    legend.position = "bottom",
    plot.title = element_text(hjust = 0.5)
  ) +
  guides(
    linetype = "none",
    color = guide_legend(
      title = "Eye-tr.",
      ncol = 4,
      byrow = TRUE,
      override.aes = list(
        linetype = c("dotdash", "dotdash", "solid", "solid"),
        shape = c(16, 17, 16, 17)
      )
    ),
    shape = "none"
  )
ggsave(paste0("./images/et_regression_interaction.pdf"), device="pdf", height=6, width=16/3)
```




